US 12,608,593 B2Grant
Compressing image-to-image models
Issue Date:2026-04-21
•20 Claims
•11 Drawing Sheets
Abstract
Systems and methods herein describe an image compression system. The image compression system generates a first generative adversarial network (GAN), identifies a threshold, based on the threshold, generates a second GAN by pruning channels of the first GAN, trains the second GAN using similarity-based knowledge distillation from the first GAN, and stores the trained second GAN.
Metadata
Assignee
- Snap Inc.
Inventors
- Jian Ren
- Oliver Woodford
- Sergey Tulyakov
- Jiazhuo Wang
- Qing Jin
Application Information
Application Number:US 17/558,327
Filing Date:2021-12-21
Priority Date:2020-12-29
Art Unit:2129
Classifications
IPC:
G06N3/04G06N3/045G06N3/08G06N3/088
Field of Search:
G06N 3/045G06N 3/088
Patent Drawings (11 sheets)
Description
CLAIM OF PRIORITY
[0001] This application claims the benefit of priority to U.S. Provisional Patent Application Ser. No. 63/131,613, filed Dec. 29, 2020, which is hereby incorporated by reference herein in its entirety.
TECHNICAL FIELD
[0002] Embodiments herein generally relate to generative adversarial network. Specifically, but not limited to, methods and systems describe compressing image-to-image models.
BACKGROUND
[0003] A Generative Adversarial Networks (GAN) is a machine learning framework in which two neural networks: a discriminator network and a generator network, contest with each other in a zero-sum game. Given a training data set, a GAN trained model learns to generate new data with the same statistics as the training set.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0004] In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced. Some nonlimiting examples are illustrated in the figures of the accompanying drawings in which:
[0005] FIG. 1 is a diagrammatic representation of a networked environment in which the present disclosure may be deployed, in accordance with some examples.
[0006] FIG. 2 is a diagrammatic representation of a messaging system, in accordance with some examples, that has both client-side and server-side functionality.
[0007] FIG. 3 is a diagrammatic representation of a data structure as maintained in a database, in accordance with some examples.
[0008] FIG. 4 is a diagrammatic representation of a message, in accordance with some examples.
[0009] FIG. 5 is a flowchart for an access-limiting process, in accordance with some examples.
[0010] FIG. 6 is an illustration of a generative adversarial network architecture, according to some example embodiments.
[0011] FIG. 7 is an illustration of a residual block, according to some example embodiments.
[0012] FIG. 8 illustrates a method for generating a compressed image-to-image model in accordance with one embodiment.
[0013] FIG. 9 is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, in accordance with some examples.
[0014] FIG. 10 is a block diagram showing a software architecture within which examples may be implemented.
DETAILED DESCRIPTION
[0015] Embodiments herein describe methods and system for compressing image-to-image models. Generative Adversarial Networks (GANs) have achieved success in generating high-fidelity images. However, they suffer from low efficiency due to tremendous computational cost and bulky memory usage. Embodiments herein describe an image compression system that introduces a teacher network that provides a search space in which efficient network architectures can be found.
[0016] The image compression system includes inception-based residual blocks into the generator network of the teacher network (e.g., a first GAN). The image compression system uses a one-step pruning method to search for a student network (e.g., a second GAN) from the teacher network. The image compression system trains the student network using knowledge distillation by maximizing feature similarity between the teacher network and the student network using a similarity metric. The resulting trained student network contains less parameters than the teacher network and are more computationally efficient than its teacher network counterpart. The trained student network may run on a mobile computing device with lower computation costs than the teacher network.
Networked Computing Environment
[0017] FIG. 1 is a block diagram showing an example messaging system 100 for exchanging data (e.g., messages and associated content) over a network. The messaging system 100 includes multiple instances of a client device 102, each of which hosts a number of applications, including a messaging client 104 and other applications 106. Each messaging client 104 is communicatively coupled to other instances of the messaging client 104 (e.g., hosted on respective other client devices 102), a messaging server system 108 and third-party servers 110 via a network 112 (e.g., the Internet). A messaging client 104 can also communicate with locally-hosted applications 106 using Applications Program Interfaces (APIs).
[0018] A messaging client 104 is able to communicate and exchange data with other messaging clients 104 and with the messaging server system 108 via the network 112. The data exchanged between messaging clients 104, and between a messaging client 104 and the messaging server system 108, includes functions (e.g., commands to invoke functions) as well as payload data (e.g., text, audio, video or other multimedia data).
[0019] The messaging server system 108 provides server-side functionality via the network 112 to a particular messaging client 104. While certain functions of the messaging system 100 are described herein as being performed by either a messaging client 104 or by the messaging server system 108, the location of certain functionality either within the messaging client 104 or the messaging server system 108 may be a design choice. For example, it may be technically preferable to initially deploy certain technology and functionality within the messaging server system 108 but to later migrate this technology and functionality to the messaging client 104 where a client device 102 has sufficient processing capacity.
[0020] The messaging server system 108 supports various services and operations that are provided to the messaging client 104. Such operations include transmitting data to, receiving data from, and processing data generated by the messaging client 104. This data may include message content, client device information, geolocation information, media augmentation and overlays, message content persistence conditions, social network information, and live event information, as examples. Data exchanges within the messaging system 100 are invoked and controlled through functions available via user interfaces (UIs) of the messaging client 104.
[0021] Turning now specifically to the messaging server system 108, an Application Program Interface (API) server 116 is coupled to, and provides a programmatic interface to, application servers 114. The application servers 114 are communicatively coupled to a database server 120, which facilitates access to a database 126 that stores data associated with messages processed by the application servers 114. Similarly, a web server 128 is coupled to the application servers 114, and provides web-based interfaces to the application servers 114. To this end, the web server 128 processes incoming network requests over the Hypertext Transfer Protocol (HTTP) and several other related protocols.
[0022] The Application Program Interface (API) server 116 receives and transmits message data (e.g., commands and message payloads) between the client device 102 and the application servers 114. Specifically, the Application Program Interface (API) server 116 provides a set of interfaces (e.g., routines and protocols) that can be called or queried by the messaging client 104 in order to invoke functionality of the application servers 114. The Application Program Interface (API) server 116 exposes various functions supported by the application servers 114, including account registration, login functionality, the sending of messages, via the application servers 114, from a particular messaging client 104 to another messaging client 104, the sending of media files (e.g., images or video) from a messaging client 104 to a messaging server 118, and for possible access by another messaging client 104, the settings of a collection of media data (e.g., story), the retrieval of a list of friends of a user of a client device 102, the retrieval of such collections, the retrieval of messages and content, the addition and deletion of entities (e.g., friends) to an entity graph (e.g., a social graph), the location of friends within a social graph, and opening an application event (e.g., relating to the messaging client 104).
[0023] The application servers 114 host a number of server applications and subsystems, including for example a messaging server 118, an image processing server 122, a social network server 124 and an image compression system 130. The messaging server 118 implements a number of message processing technologies and functions, particularly related to the aggregation and other processing of content (e.g., textual and multimedia content) included in messages received from multiple instances of the messaging client 104. As will be described in further detail, the text and media content from multiple sources may be aggregated into collections of content (e.g., called stories or galleries). These collections are then made available to the messaging client 104. Other processor and memory intensive processing of data may also be performed server-side by the messaging server 118, in view of the hardware requirements for such processing.
[0024] The application servers 114 also include an image processing server 122 that is dedicated to performing various image processing operations, typically with respect to images or video within the payload of a message sent from or received at the messaging server 118.
[0025] The social network server 124 supports various social networking functions and services and makes these functions and services available to the messaging server 118. To this end, the social network server 124 maintains and accesses an entity graph 308 (as shown in FIG. 3 ) within the database 126. Examples of functions and services supported by the social network server 124 include the identification of other users of the messaging system 100 with which a particular user has relationships or is “following,” and also the identification of other entities and interests of a particular user.
[0026] The image compression system 130 searches a teacher network to generate a computationally efficient, student network. The image compression system 130 leverages a residual block that is specially configured to generate a search space from which an efficient student network may be found.
[0027] Returning to the messaging client 104, features and functions of an external resource (e.g., an application 106 or applet) are made available to a user via an interface of the messaging client 104. In this context, “external” refers to the fact that the application 106 or applet is external to the messaging client 104. The external resource is often provided by a third party but may also be provided by the creator or provider of the messaging client 104. The messaging client 104 receives a user selection of an option to launch or access features of such an external resource. The external resource may be the application 106 installed on the client device 102 (e.g., a “native app”), or a small-scale version of the application (e.g., an “applet”) that is hosted on the client device 102 or remote of the client device 102 (e.g., on third-party servers 110). The small-scale version of the application includes a subset of features and functions of the application (e.g., the full-scale, native version of the application) and is implemented using a markup-language document. In one example, the small-scale version of the application (e.g., an “applet”) is a web-based, markup-language version of the application and is embedded in the messaging client 104. In addition to using markup-language documents (e.g., a .*ml file), an applet may incorporate a scripting language (e.g., a .*js file or a .json file) and a style sheet (e.g., a .*ss file).
[0028] In response to receiving a user selection of the option to launch or access features of the external resource, the messaging client 104 determines whether the selected external resource is a web-based external resource or a locally-installed application 106. In some cases, applications 106 that are locally installed on the client device 102 can be launched independently of and separately from the messaging client 104, such as by selecting an icon, corresponding to the application 106, on a home screen of the client device 102. Small-scale versions of such applications can be launched or accessed via the messaging client 104 and, in some examples, no or limited portions of the small-scale application can be accessed outside of the messaging client 104. The small-scale application can be launched by the messaging client 104 receiving, from a third-party server 110 for example, a markup-language document associated with the small-scale application and processing such a document.
[0029] In response to determining that the external resource is a locally-installed application 106, the messaging client 104 instructs the client device 102 to launch the external resource by executing locally-stored code corresponding to the external resource. In response to determining that the external resource is a web-based resource, the messaging client 104 communicates with the third-party servers 110 (for example) to obtain a markup-language document corresponding to the selected external resource. The messaging client 104 then processes the obtained markup-language document to present the web-based external resource within a user interface of the messaging client 104.
[0030] The messaging client 104 can notify a user of the client device 102, or other users related to such a user (e.g., “friends”), of activity taking place in one or more external resources. For example, the messaging client 104 can provide participants in a conversation (e.g., a chat session) in the messaging client 104 with notifications relating to the current or recent use of an external resource by one or more members of a group of users. One or more users can be invited to join in an active external resource or to launch a recently-used but currently inactive (in the group of friends) external resource. The external resource can provide participants in a conversation, each using respective messaging clients 104, with the ability to share an item, status, state, or location in an external resource with one or more members of a group of users into a chat session. The shared item may be an interactive chat card with which members of the chat can interact, for example, to launch the corresponding external resource, view specific information within the external resource, or take the member of the chat to a specific location or state within the external resource. Within a given external resource, response messages can be sent to users on the messaging client 104. The external resource can selectively include different media items in the responses, based on a current context of the external resource.
[0031] The messaging client 104 can present a list of the available external resources (e.g., applications 106 or applets) to a user to launch or access a given external resource. This list can be presented in a context-sensitive menu. For example, the icons representing different ones of the application 106 (or applets) can vary based on how the menu is launched by the user (e.g., from a conversation interface or from a non-conversation interface).
System Architecture
[0032] FIG. 2 is a block diagram illustrating further details regarding the messaging system 100, according to some examples. Specifically, the messaging system 100 is shown to comprise the messaging client 104 and the application servers 114. The messaging system 100 embodies a number of subsystems, which are supported on the client-side by the messaging client 104 and on the sever-side by the application servers 114. These subsystems include, for example, an ephemeral timer system 202, a collection management system 204, an augmentation system 208, a map system 210, a game system 212, an external resource system 214, and an image compression system 130.
[0033] The ephemeral timer system 202 is responsible for enforcing the temporary or time-limited access to content by the messaging client 104 and the messaging server 118. The ephemeral timer system 202 incorporates a number of timers that, based on duration and display parameters associated with a message, or collection of messages (e.g., a story), selectively enable access (e.g., for presentation and display) to messages and associated content via the messaging client 104. Further details regarding the operation of the ephemeral timer system 202 are provided below.
[0034] The collection management system 204 is responsible for managing sets or collections of media (e.g., collections of text, image video, and audio data). A collection of content (e.g., messages, including images, video, text, and audio) may be organized into an “event gallery” or an “event story.” Such a collection may be made available for a specified time period, such as the duration of an event to which the content relates. For example, content relating to a music concert may be made available as a “story” for the duration of that music concert. The collection management system 204 may also be responsible for publishing an icon that provides notification of the existence of a particular collection to the user interface of the messaging client 104.
[0035] The collection management system 204 furthermore includes a curation interface 206 that allows a collection manager to manage and curate a particular collection of content. For example, the curation interface 206 enables an event organizer to curate a collection of content relating to a specific event (e.g., delete inappropriate content or redundant messages). Additionally, the collection management system 204 employs machine vision (or image recognition technology) and content rules to automatically curate a content collection. In certain examples, compensation may be paid to a user for the inclusion of user-generated content into a collection. In such cases, the collection management system 204 operates to automatically make payments to such users for the use of their content.
[0036] The augmentation system 208 provides various functions that enable a user to augment (e.g., annotate or otherwise modify or edit) media content associated with a message. For example, the augmentation system 208 provides functions related to the generation and publishing of media overlays for messages processed by the messaging system 100. The augmentation system 208 operatively supplies a media overlay or augmentation (e.g., an image filter) to the messaging client 104 based on a geolocation of the client device 102. In another example, the augmentation system 208 operatively supplies a media overlay to the messaging client 104 based on other information, such as social network information of the user of the client device 102. A media overlay may include audio and visual content and visual effects. Examples of audio and visual content include pictures, texts, logos, animations, and sound effects. An example of a visual effect includes color overlaying. The audio and visual content or the visual effects can be applied to a media content item (e.g., a photo) at the client device 102. For example, the media overlay may include text or image that can be overlaid on top of a photograph taken by the client device 102. In another example, the media overlay includes an identification of a location overlay (e.g., Venice beach), a name of a live event, or a name of a merchant overlay (e.g., Beach Coffee House). In another example, the augmentation system 208 uses the geolocation of the client device 102 to identify a media overlay that includes the name of a merchant at the geolocation of the client device 102. The media overlay may include other indicia associated with the merchant. The media overlays may be stored in the database 126 and accessed through the database server 120.
[0037] In some examples, the augmentation system 208 provides a user-based publication platform that enables users to select a geolocation on a map and upload content associated with the selected geolocation. The user may also specify circumstances under which a particular media overlay should be offered to other users. The augmentation system 208 generates a media overlay that includes the uploaded content and associates the uploaded content with the selected geolocation.
[0038] In other examples, the augmentation system 208 provides a merchant-based publication platform that enables merchants to select a particular media overlay associated with a geolocation via a bidding process. For example, the augmentation system 208 associates the media overlay of the highest bidding merchant with a corresponding geolocation for a predefined amount of time.
[0039] The map system 210 provides various geographic location functions and supports the presentation of map-based media content and messages by the messaging client 104. For example, the map system 210 enables the display of user icons or avatars (e.g., stored in profile data 316) on a map to indicate a current or past location of “friends” of a user, as well as media content (e.g., collections of messages including photographs and videos) generated by such friends, within the context of a map. For example, a message posted by a user to the messaging system 100 from a specific geographic location may be displayed within the context of a map at that particular location to “friends” of a specific user on a map interface of the messaging client 104. A user can furthermore share his or her location and status information (e.g., using an appropriate status avatar) with other users of the messaging system 100 via the messaging client 104, with this location and status information being similarly displayed within the context of a map interface of the messaging client 104 to selected users.
[0040] The game system 212 provides various gaming functions within the context of the messaging client 104. The messaging client 104 provides a game interface providing a list of available games that can be launched by a user within the context of the messaging client 104 and played with other users of the messaging system 100. The messaging system 100 further enables a particular user to invite other users to participate in the play of a specific game, by issuing invitations to such other users from the messaging client 104. The messaging client 104 also supports both the voice and text messaging (e.g., chats) within the context of gameplay, provides a leaderboard for the games, and also supports the provision of in-game rewards (e.g., coins and items).
[0041] The external resource system 214 provides an interface for the messaging client 104 to communicate with remote servers (e.g. third-party servers 110) to launch or access external resources, i.e. applications or applets. Each third-party server 110 hosts, for example, a markup language (e.g., HTML5) based application or small-scale version of an application (e.g., game, utility, payment, or ride-sharing application). The messaging client 104 may launches a web-based resource (e.g., application) by accessing the HTML5 file from the third-party servers 110 associated with the web-based resource. In certain examples, applications hosted by third-party servers 110 are programmed in JavaScript leveraging a Software Development Kit (SDK) provided by the messaging server 118. The SDK includes Application Programming Interfaces (APIs) with functions that can be called or invoked by the web-based application. In certain examples, the messaging server 118 includes a JavaScript library that provides a given external resource access to certain user data of the messaging client 104. HTML5 is used as an example technology for programming games, but applications and resources programmed based on other technologies can be used.
[0042] In order to integrate the functions of the SDK into the web-based resource, the SDK is downloaded by a third-party server 110 from the messaging server 118 or is otherwise received by the third-party server 110. Once downloaded or received, the SDK is included as part of the application code of a web-based external resource. The code of the web-based resource can then call or invoke certain functions of the SDK to integrate features of the messaging client 104 into the web-based resource.
[0043] The SDK stored on the messaging server 118 effectively provides the bridge between an external resource (e.g., applications 106 or applets and the messaging client 104. This provides the user with a seamless experience of communicating with other users on the messaging client 104, while also preserving the look and feel of the messaging client 104. To bridge communications between an external resource and a messaging client 104, in certain examples, the SDK facilitates communication between third-party servers 110 and the messaging client 104. In certain examples, a WebViewJavaScriptBridge running on a client device 102 establishes two one-way communication channels between an external resource and the messaging client 104. Messages are sent between the external resource and the messaging client 104 via these communication channels asynchronously. Each SDK function invocation is sent as a message and callback. Each SDK function is implemented by constructing a unique callback identifier and sending a message with that callback identifier.
[0044] By using the SDK, not all information from the messaging client 104 is shared with third-party servers 110. The SDK limits which information is shared based on the needs of the external resource. In certain examples, each third-party server 110 provides an HTML5 file corresponding to the web-based external resource to the messaging server 118. The messaging server 118 can add a visual representation (such as a box art or other graphic) of the web-based external resource in the messaging client 104. Once the user selects the visual representation or instructs the messaging client 104 through a GUI of the messaging client 104 to access features of the web-based external resource, the messaging client 104 obtains the HTML5 file and instantiates the resources necessary to access the features of the web-based external resource.
[0045] The messaging client 104 presents a graphical user interface (e.g., a landing page or title screen) for an external resource. During, before, or after presenting the landing page or title screen, the messaging client 104 determines whether the launched external resource has been previously authorized to access user data of the messaging client 104. In response to determining that the launched external resource has been previously authorized to access user data of the messaging client 104, the messaging client 104 presents another graphical user interface of the external resource that includes functions and features of the external resource. In response to determining that the launched external resource has not been previously authorized to access user data of the messaging client 104, after a threshold period of time (e.g., 3 seconds) of displaying the landing page or title screen of the external resource, the messaging client 104 slides up (e.g., animates a menu as surfacing from a bottom of the screen to a middle of or other portion of the screen) a menu for authorizing the external resource to access the user data. The menu identifies the type of user data that the external resource will be authorized to use. In response to receiving a user selection of an accept option, the messaging client 104 adds the external resource to a list of authorized external resources and allows the external resource to access user data from the messaging client 104. In some examples, the external resource is authorized by the messaging client 104 to access the user data in accordance with an OAuth 2 framework.
[0046] The messaging client 104 controls the type of user data that is shared with external resources based on the type of external resource being authorized. For example, external resources that include full-scale applications (e.g., an application 106) are provided with access to a first type of user data (e.g., only two-dimensional avatars of users with or without different avatar characteristics). As another example, external resources that include small-scale versions of applications (e.g., web-based versions of applications) are provided with access to a second type of user data (e.g., payment information, two-dimensional avatars of users, three-dimensional avatars of users, and avatars with various avatar characteristics). Avatar characteristics include different ways to customize a look and feel of an avatar, such as different poses, facial features, clothing, and so forth.
[0047] The image compression system 130 searches a teacher network to generate a computationally efficient, student network. The image compression system 130 leverages a residual block that is specially configured to generate a search space from which an efficient student network may be found. Aspects of the image compression system 130 may exist on the messaging client 104 and other aspects may exist on the application servers 114. In some examples, the image compression system 130 operates exclusively on the messaging client 104.
Data Architecture
[0048] FIG. 3 is a schematic diagram illustrating data structures 300, which may be stored in the database 126 of the messaging server system 108, according to certain examples. While the content of the database 126 is shown to comprise a number of tables, it will be appreciated that the data could be stored in other types of data structures (e.g., as an object-oriented database).
[0049] The database 126 includes message data stored within a message table 302. This message data includes, for any particular one message, at least message sender data, message recipient (or receiver) data, and a payload. Further details regarding information that may be included in a message, and included within the message data stored in the message table 302 is described below with reference to FIG. 4 .
[0050] An entity table 306 stores entity data, and is linked (e.g., referentially) to an entity graph 308 and profile data 316. Entities for which records are maintained within the entity table 306 may include individuals, corporate entities, organizations, objects, places, events, and so forth. Regardless of entity type, any entity regarding which the messaging server system 108 stores data may be a recognized entity. Each entity is provided with a unique identifier, as well as an entity type identifier (not shown).
[0051] The entity graph 308 stores information regarding relationships and associations between entities. Such relationships may be social, professional (e.g., work at a common corporation or organization) interested-based or activity-based, merely for example.
[0052] The profile data 316 stores multiple types of profile data about a particular entity. The profile data 316 may be selectively used and presented to other users of the messaging system 100, based on privacy settings specified by a particular entity. Where the entity is an individual, the profile data 316 includes, for example, a user name, telephone number, address, settings (e.g., notification and privacy settings), as well as a user-selected avatar representation (or collection of such avatar representations). A particular user may then selectively include one or more of these avatar representations within the content of messages communicated via the messaging system 100, and on map interfaces displayed by messaging clients 104 to other users. The collection of avatar representations may include “status avatars,” which present a graphical representation of a status or activity that the user may select to communicate at a particular time.
[0053] Where the entity is a group, the profile data 316 for the group may similarly include one or more avatar representations associated with the group, in addition to the group name, members, and various settings (e.g., notifications) for the relevant group.
[0054] The database 126 also stores augmentation data, such as overlays or filters, in an augmentation table 310. The augmentation data is associated with and applied to videos (for which data is stored in a video table 304) and images (for which data is stored in an image table 312).
[0055] Filters, in one example, are overlays that are displayed as overlaid on an image or video during presentation to a recipient user. Filters may be of various types, including user-selected filters from a set of filters presented to a sending user by the messaging client 104 when the sending user is composing a message. Other types of filters include geolocation filters (also known as geo-filters), which may be presented to a sending user based on geographic location. For example, geolocation filters specific to a neighborhood or special location may be presented within a user interface by the messaging client 104, based on geolocation information determined by a Global Positioning System (GPS) unit of the client device 102.
[0056] Another type of filter is a data filter, which may be selectively presented to a sending user by the messaging client 104, based on other inputs or information gathered by the client device 102 during the message creation process. Examples of data filters include current temperature at a specific location, a current speed at which a sending user is traveling, battery life for a client device 102, or the current time.
[0057] Other augmentation data that may be stored within the image table 312 includes augmented reality content items (e.g., corresponding to applying Lenses or augmented reality experiences). An augmented reality content item may be a real-time special effect and sound that may be added to an image or a video.
[0058] As described above, augmentation data includes augmented reality content items, overlays, image transformations, AR images, and similar terms refer to modifications that may be applied to image data (e.g., videos or images). This includes real-time modifications, which modify an image as it is captured using device sensors (e.g., one or multiple cameras) of a client device 102 and then displayed on a screen of the client device 102 with the modifications. This also includes modifications to stored content, such as video clips in a gallery that may be modified. For example, in a client device 102 with access to multiple augmented reality content items, a user can use a single video clip with multiple augmented reality content items to see how the different augmented reality content items will modify the stored clip. For example, multiple augmented reality content items that apply different pseudorandom movement models can be applied to the same content by selecting different augmented reality content items for the content. Similarly, real-time video capture may be used with an illustrated modification to show how video images currently being captured by sensors of a client device 102 would modify the captured data. Such data may simply be displayed on the screen and not stored in memory, or the content captured by the device sensors may be recorded and stored in memory with or without the modifications (or both). In some systems, a preview feature can show how different augmented reality content items will look within different windows in a display at the same time. This can, for example, enable multiple windows with different pseudorandom animations to be viewed on a display at the same time.
[0059] Data and various systems using augmented reality content items or other such transform systems to modify content using this data can thus involve detection of objects (e.g., faces, hands, bodies, cats, dogs, surfaces, objects, etc.), tracking of such objects as they leave, enter, and move around the field of view in video frames, and the modification or transformation of such objects as they are tracked. In various examples, different methods for achieving such transformations may be used. Some examples may involve generating a three-dimensional mesh model of the object or objects, and using transformations and animated textures of the model within the video to achieve the transformation. In other examples, tracking of points on an object may be used to place an image or texture (which may be two dimensional or three dimensional) at the tracked position. In still further examples, neural network analysis of video frames may be used to place images, models, or textures in content (e.g., images or frames of video). Augmented reality content items thus refer both to the images, models, and textures used to create transformations in content, as well as to additional modeling and analysis information needed to achieve such transformations with object detection, tracking, and placement.
[0060] Real-time video processing can be performed with any kind of video data (e.g., video streams, video files, etc.) saved in a memory of a computerized system of any kind. For example, a user can load video files and save them in a memory of a device, or can generate a video stream using sensors of the device. Additionally, any objects can be processed using a computer animation model, such as a human's face and parts of a human body, animals, or non-living things such as chairs, cars, or other objects.
[0061] In some examples, when a particular modification is selected along with content to be transformed, elements to be transformed are identified by the computing device, and then detected and tracked if they are present in the frames of the video. The elements of the object are modified according to the request for modification, thus transforming the frames of the video stream. Transformation of frames of a video stream can be performed by different methods for different kinds of transformation. For example, for transformations of frames mostly referring to changing forms of object's elements characteristic points for each element of an object are calculated (e.g., using an Active Shape Model (ASM) or other known methods). Then, a mesh based on the characteristic points is generated for each of the at least one element of the object. This mesh used in the following stage of tracking the elements of the object in the video stream. In the process of tracking, the mentioned mesh for each element is aligned with a position of each element. Then, additional points are generated on the mesh. A first set of first points is generated for each element based on a request for modification, and a set of second points is generated for each element based on the set of first points and the request for modification. Then, the frames of the video stream can be transformed by modifying the elements of the object on the basis of the sets of first and second points and the mesh. In such method, a background of the modified object can be changed or distorted as well by tracking and modifying the background.
[0062] In some examples, transformations changing some areas of an object using its elements can be performed by calculating characteristic points for each element of an object and generating a mesh based on the calculated characteristic points. Points are generated on the mesh, and then various areas based on the points are generated. The elements of the object are then tracked by aligning the area for each element with a position for each of the at least one element, and properties of the areas can be modified based on the request for modification, thus transforming the frames of the video stream. Depending on the specific request for modification properties of the mentioned areas can be transformed in different ways. Such modifications may involve changing color of areas; removing at least some part of areas from the frames of the video stream; including one or more new objects into areas which are based on a request for modification; and modifying or distorting the elements of an area or object. In various examples, any combination of such modifications or other similar modifications may be used. For certain models to be animated, some characteristic points can be selected as control points to be used in determining the entire state-space of options for the model animation.
[0063] In some examples of a computer animation model to transform image data using face detection, the face is detected on an image with use of a specific face detection algorithm (e.g., Viola-Jones). Then, an Active Shape Model (ASM) algorithm is applied to the face region of an image to detect facial feature reference points.
[0064] Other methods and algorithms suitable for face detection can be used. For example, in some examples, features are located using a landmark, which represents a distinguishable point present in most of the images under consideration. For facial landmarks, for example, the location of the left eye pupil may be used. If an initial landmark is not identifiable (e.g., if a person has an eyepatch), secondary landmarks may be used. Such landmark identification procedures may be used for any such objects. In some examples, a set of landmarks forms a shape. Shapes can be represented as vectors using the coordinates of the points in the shape. One shape is aligned to another with a similarity transform (allowing translation, scaling, and rotation) that minimizes the average Euclidean distance between shape points. The mean shape is the mean of the aligned training shapes.
[0065] In some examples, a search for landmarks from the mean shape aligned to the position and size of the face determined by a global face detector is started. Such a search then repeats the steps of suggesting a tentative shape by adjusting the locations of shape points by template matching of the image texture around each point and then conforming the tentative shape to a global shape model until convergence occurs. In some systems, individual template matches are unreliable, and the shape model pools the results of the weak template matches to form a stronger overall classifier. The entire search is repeated at each level in an image pyramid, from coarse to fine resolution.
[0066] A transformation system can capture an image or video stream on a client device (e.g., the client device 102) and perform complex image manipulations locally on the client device 102 while maintaining a suitable user experience, computation time, and power consumption. The complex image manipulations may include size and shape changes, emotion transfers (e.g., changing a face from a frown to a smile), state transfers (e.g., aging a subject, reducing apparent age, changing gender), style transfers, graphical element application, and any other suitable image or video manipulation implemented by a convolutional neural network that has been configured to execute efficiently on the client device 102.
[0067] In some examples, a computer animation model to transform image data can be used by a system where a user may capture an image or video stream of the user (e.g., a selfie) using a client device 102 having a neural network operating as part of a messaging client 104 operating on the client device 102. The transformation system operating within the messaging client 104 determines the presence of a face within the image or video stream and provides modification icons associated with a computer animation model to transform image data, or the computer animation model can be present as associated with an interface described herein. The modification icons include changes that may be the basis for modifying the user's face within the image or video stream as part of the modification operation. Once a modification icon is selected, the transform system initiates a process to convert the image of the user to reflect the selected modification icon (e.g., generate a smiling face on the user). A modified image or video stream may be presented in a graphical user interface displayed on the client device 102 as soon as the image or video stream is captured, and a specified modification is selected. The transformation system may implement a complex convolutional neural network on a portion of the image or video stream to generate and apply the selected modification. That is, the user may capture the image or video stream and be presented with a modified result in real-time or near real-time once a modification icon has been selected. Further, the modification may be persistent while the video stream is being captured, and the selected modification icon remains toggled. Machine taught neural networks may be used to enable such modifications.
[0068] The graphical user interface, presenting the modification performed by the transform system, may supply the user with additional interaction options. Such options may be based on the interface used to initiate the content capture and selection of a particular computer animation model (e.g., initiation from a content creator user interface). In various examples, a modification may be persistent after an initial selection of a modification icon. The user may toggle the modification on or off by tapping or otherwise selecting the face being modified by the transformation system and store it for later viewing or browse to other areas of the imaging application. Where multiple faces are modified by the transformation system, the user may toggle the modification on or off globally by tapping or selecting a single face modified and displayed within a graphical user interface. In some examples, individual faces, among a group of multiple faces, may be individually modified, or such modifications may be individually toggled by tapping or selecting the individual face or a series of individual faces displayed within the graphical user interface.
[0069] A story table 314 stores data regarding collections of messages and associated image, video, or audio data, which are compiled into a collection (e.g., a story or a gallery). The creation of a particular collection may be initiated by a particular user (e.g., each user for which a record is maintained in the entity table 306). A user may create a “personal story” in the form of a collection of content that has been created and sent/broadcast by that user. To this end, the user interface of the messaging client 104 may include an icon that is user-selectable to enable a sending user to add specific content to his or her personal story.
[0070] A collection may also constitute a “live story,” which is a collection of content from multiple users that is created manually, automatically, or using a combination of manual and automatic techniques. For example, a “live story” may constitute a curated stream of user-submitted content from varies locations and events. Users whose client devices have location services enabled and are at a common location event at a particular time may, for example, be presented with an option, via a user interface of the messaging client 104, to contribute content to a particular live story. The live story may be identified to the user by the messaging client 104, based on his or her location. The end result is a “live story” told from a community perspective.
[0071] A further type of content collection is known as a “location story,” which enables a user whose client device 102 is located within a specific geographic location (e.g., on a college or university campus) to contribute to a particular collection. In some examples, a contribution to a location story may require a second degree of authentication to verify that the end user belongs to a specific organization or other entity (e.g., is a student on the university campus).
[0072] As mentioned above, the video table 304 stores video data that, in one example, is associated with messages for which records are maintained within the message table 302. Similarly, the image table 312 stores image data associated with messages for which message data is stored in the entity table 306. The entity table 306 may associate various augmentations from the augmentation table 310 with various images and videos stored in the image table 312 and the video table 304.
Data Communications Architecture
[0073] FIG. 4 is a schematic diagram illustrating a structure of a message 400, according to some examples, generated by a messaging client 104 for communication to a further messaging client 104 or the messaging server 118. The content of a particular message 400 is used to populate the message table 302 stored within the database 126, accessible by the messaging server 118. Similarly, the content of a message 400 is stored in memory as “in-transit” or “in-flight” data of the client device 102 or the application servers 114. A message 400 is shown to include the following example components:
-
- [0074] message identifier 402: a unique identifier that identifies the message 400.
- [0075] message text payload 404: text, to be generated by a user via a user interface of the client device 102, and that is included in the message 400.
- [0076] message image payload 406: image data, captured by a camera component of a client device 102 or retrieved from a memory component of a client device 102, and that is included in the message 400. Image data for a sent or received message 400 may be stored in the image table 312.
- [0077] message video payload 408: video data, captured by a camera component or retrieved from a memory component of the client device 102, and that is included in the message 400. Video data for a sent or received message 400 may be stored in the video table 304.
- [0078] message audio payload 410: audio data, captured by a microphone or retrieved from a memory component of the client device 102, and that is included in the message 400.
- [0079] message augmentation data 412: augmentation data (e.g., filters, stickers, or other annotations or enhancements) that represents augmentations to be applied to message image payload 406, message video payload 408, or message audio payload 410 of the message 400. Augmentation data for a sent or received message 400 may be stored in the augmentation table 310.
- [0080] message duration parameter 414: parameter value indicating, in seconds, the amount of time for which content of the message (e.g., the message image payload 406, message video payload 408, message audio payload 410) is to be presented or made accessible to a user via the messaging client 104.
- [0081] message geolocation parameter 416: geolocation data (e.g., latitudinal and longitudinal coordinates) associated with the content payload of the message. Multiple message geolocation parameter 416 values may be included in the payload, each of these parameter values being associated with respect to content items included in the content (e.g., a specific image into within the message image payload 406, or a specific video in the message video payload 408).
- [0082] message story identifier 418: identifier values identifying one or more content collections (e.g., “stories” identified in the story table 314) with which a particular content item in the message image payload 406 of the message 400 is associated. For example, multiple images within the message image payload 406 may each be associated with multiple content collections using identifier values.
- [0083] message tag 420: each message 400 may be tagged with multiple tags, each of which is indicative of the subject matter of content included in the message payload. For example, where a particular image included in the message image payload 406 depicts an animal (e.g., a lion), a tag value may be included within the message tag 420 that is indicative of the relevant animal. Tag values may be generated manually, based on user input, or may be automatically generated using, for example, image recognition.
- [0084] message sender identifier 422: an identifier (e.g., a messaging system identifier, email address, or device identifier) indicative of a user of the Client device 102 on which the message 400 was generated and from which the message 400 was sent.
- [0085] message receiver identifier 424: an identifier (e.g., a messaging system identifier, email address, or device identifier) indicative of a user of the client device 102 to which the message 400 is addressed.
[0086] The contents (e.g., values) of the various components of message 400 may be pointers to locations in tables within which content data values are stored. For example, an image value in the message image payload 406 may be a pointer to (or address of) a location within an image table 312. Similarly, values within the message video payload 408 may point to data stored within a video table 304, values stored within the message augmentations 412 may point to data stored in an augmentation table 310, values stored within the message story identifier 418 may point to data stored in a story table 314, and values stored within the message sender identifier 422 and the message receiver identifier 424 may point to user records stored within an entity table 306.
Time-Based Access Limitation Architecture
[0087] FIG. 5 is a schematic diagram illustrating an access-limiting process 500, in terms of which access to content (e.g., an ephemeral message 502, and associated multimedia payload of data) or a content collection (e.g., an ephemeral message group 504) may be time-limited (e.g., made ephemeral).
[0088] An ephemeral message 502 is shown to be associated with a message duration parameter 506, the value of which determines an amount of time that the ephemeral message 502 will be displayed to a receiving user of the ephemeral message 502 by the messaging client 104. In one example, an ephemeral message 502 is viewable by a receiving user for up to a maximum of 10 seconds, depending on the amount of time that the sending user specifies using the message duration parameter 506.
[0089] The message duration parameter 506 and the message receiver identifier 424 are shown to be inputs to a message timer 510, which is responsible for determining the amount of time that the ephemeral message 502 is shown to a particular receiving user identified by the message receiver identifier 424. In particular, the ephemeral message 502 will only be shown to the relevant receiving user for a time period determined by the value of the message duration parameter 506. The message timer 510 is shown to provide output to a more generalized ephemeral timer system 202, which is responsible for the overall timing of display of content (e.g., an ephemeral message 502) to a receiving user.
[0090] The ephemeral message 502 is shown in FIG. 5 to be included within an ephemeral message group 504 (e.g., a collection of messages in a personal story, or an event story). The ephemeral message group 504 has an associated group duration parameter 508, a value of which determines a time duration for which the ephemeral message group 504 is presented and accessible to users of the messaging system 100. The group duration parameter 508, for example, may be the duration of a music concert, where the ephemeral message group 504 is a collection of content pertaining to that concert. Alternatively, a user (either the owning user or a curator user) may specify the value for the group duration parameter 508 when performing the setup and creation of the ephemeral message group 504.
[0091] Additionally, each ephemeral message 502 within the ephemeral message group 504 has an associated group participation parameter 512, a value of which determines the duration of time for which the ephemeral message 502 will be accessible within the context of the ephemeral message group 504. Accordingly, a particular ephemeral message group 504 may “expire” and become inaccessible within the context of the ephemeral message group 504, prior to the ephemeral message group 504 itself expiring in terms of the group duration parameter 508. The group duration parameter 508, group participation parameter 512, and message receiver identifier 424 each provide input to a group timer 514, which operationally determines, firstly, whether a particular ephemeral message 502 of the ephemeral message group 504 will be displayed to a particular receiving user and, if so, for how long. Note that the ephemeral message group 504 is also aware of the identity of the particular receiving user as a result of the message receiver identifier 424.
[0092] Accordingly, the group timer 514 operationally controls the overall lifespan of an associated ephemeral message group 504, as well as an individual ephemeral message 502 included in the ephemeral message group 504. In one example, each and every ephemeral message 502 within the ephemeral message group 504 remains viewable and accessible for a time period specified by the group duration parameter 508. In a further example, a certain ephemeral message 502 may expire, within the context of ephemeral message group 504, based on a group participation parameter 512. Note that a message duration parameter 506 may still determine the duration of time for which a particular ephemeral message 502 is displayed to a receiving user, even within the context of the ephemeral message group 504. Accordingly, the message duration parameter 506 determines the duration of time that a particular ephemeral message 502 is displayed to a receiving user, regardless of whether the receiving user is viewing that ephemeral message 502 inside or outside the context of an ephemeral message group 504.
[0093] The ephemeral timer system 202 may furthermore operationally remove a particular ephemeral message 502 from the ephemeral message group 504 based on a determination that it has exceeded an associated group participation parameter 512. For example, when a sending user has established a group participation parameter 512 of 24 hours from posting, the ephemeral timer system 202 will remove the relevant ephemeral message 502 from the ephemeral message group 504 after the specified 24 hours. The ephemeral timer system 202 also operates to remove an ephemeral message group 504 when either the group participation parameter 512 for each and every ephemeral message 502 within the ephemeral message group 504 has expired, or when the ephemeral message group 504 itself has expired in terms of the group duration parameter 508.
[0094] In certain use cases, a creator of a particular ephemeral message group 504 may specify an indefinite group duration parameter 508. In this case, the expiration of the group participation parameter 512 for the last remaining ephemeral message 502 within the ephemeral message group 504 will determine when the ephemeral message group 504 itself expires. In this case, a new ephemeral message 502, added to the ephemeral message group 504, with a new group participation parameter 512, effectively extends the life of an ephemeral message group 504 to equal the value of the group participation parameter 512.
[0095] Responsive to the ephemeral timer system 202 determining that an ephemeral message group 504 has expired (e.g., is no longer accessible), the ephemeral timer system 202 communicates with the messaging system 100 (and, for example, specifically the messaging client 104) to cause an indicium (e.g., an icon) associated with the relevant ephemeral message group 504 to no longer be displayed within a user interface of the messaging client 104. Similarly, when the ephemeral timer system 202 determines that the message duration parameter 506 for a particular ephemeral message 502 has expired, the ephemeral timer system 202 causes the messaging client 104 to no longer display an indicium (e.g., an icon or textual identification) associated with the ephemeral message 502.
Generative Adversarial Networks
[0096] FIG. 6 is an illustration of a generative adversarial network architecture, according to some examples. In some examples, the image compression system 130 comprises of a GAN. The generator and discriminator are neural networks. Each network can be any neural network such as an artificial neural network, a convolutional neural network, a recurrent neural network, etc. The output of the generator is linked directly the input of the discriminator. Using backpropagation, the discriminator's classification provides a signal that the generator uses to update its weights.
[0097] The discriminator 610 is a classifier that tries to distinguish real data from artificial data (e.g. Data created by the generator). The discriminator 610 is trained using two data sources: real data 606 and fake data 608. The real data 606 may comprise real human faces and the fake data 608 may comprise artificial human faces. The fake data 608 is data that is generated by the generator. During the training of the discriminator 610, the discriminator 610 classifies the real data 606 and the fake data 608. The discriminator loss 612 accommodate for the discriminator 610 misclassifying real data 606 as fake and fake data 608 as real. The discriminator 610 updates its weights (e.g., weights of the neural network layers) through backpropagation using the discriminator loss 612.
[0098] The generator 604 is a neural network that generates fake data 608 based on feedback from the discriminator. It learns to make the discriminator 610 classify the fake data 608 as real. The generator 604 takes random noise 616 as input and transforms the random noise 616 into meaningful output data. The generator loss accommodates for the generator 604 producing fake data 608 that the discriminator 610 classifies as fake. The generator 604 updates its weights through backpropagation through the discriminator 610 and the generator 604 using the generator loss 614.
[0099] In some examples the image compression system 130 uses a pre-trained GAN (e.g., PIX2PIX, CYCLEGAN, GAUGAN).
[0100] FIG. 7 is an illustration of a residual block 702 used by the image compression system 130, according to some example embodiments. The residual block 702 is an inception-based residual block. A conventional residual block in an image generator network (e.g., generator 604) only contains convolution layers with one kernel size. However, the inception-based residual block 702 contains convolutional layers with different kernel sizes (e.g., 1×1, 3×3, and 5×5). The inception-based residual block 702 incorporates depth-wise blocks (blocks 704, 706, 708) (e.g., depth-wise convolutional layers). Depth-wise convolutional layers require less computation cost without sacrificing performance and are suitable for neural networks that are deployed on mobile computing devices. The inception-based residual block 702 includes six types of operations (e.g., with two types of convolution layers and three different kernel-sizes). Normalization layers (e.g., BN and ReLU) are applied between each two consecutive convolution layers. In some examples, a normalization layer that is inserted after summing features from the six blocks and the residual connection.
[0101] The number of output channels for the first convolution layers of each operation is set to that of the original residual blocks divided by six (e.g., the number of different operations in the residual block 702). In some examples, all residual blocks in the GAN are replaced with the residual block 702.
[0102] Although the described flowcharts can show operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed. A process may correspond to a method, a procedure, an algorithm, etc. The operations of methods may be performed in whole or in part, may be performed in conjunction with some or all of the operations in other methods, and may be performed by any number of different systems, such as the systems described herein, or any portion thereof, such as a processor included in any of the systems.
[0103] FIG. 8 is an example method 800 for generating a compressed image-to-image model, according to example embodiments. The method 800 can be performed by the image compression system 130 in FIG. 1 . In one embodiment, a processor (or circuitry dedicated to performing instructed tasks) included in the image compression system 130 performs the method 800 or causes the image component to perform the method 800.
[0104] At operation 802, the image compression system 130 generates a first generative adversarial network (GAN) comprising a first type of convolutional layer, a second type of convolutional layer and a plurality of kernel sizes. For example, the first type of convolutional layer may be a conventional convolutional layer and the second type of convolutional layer may be a depth-wise convolutional layer. The plurality of kernel sizes may be, for example, 1×1, 3×3, and 5×5. At operation 804, the image compression system 130 identifies a threshold. The image compression system 130 determines a scale threshold by a binary search on the scaling factors of normalization layers from the pre-trained teacher network. For example, the image compression system 130 may temporarily prune all channels with a scaling factor magnitude smaller than the threshold and measure the computational cost of the pruned model.
[0105] If the cost is smaller than the computational budget, the model is pruned too much and the image compression system 130 searches in a lower interval to get a smaller threshold. Otherwise, the image compression system 130 searches in an upper interval to get a larger value. During this process, the image compression system 130 retains the number of output channels for convolution layers outside the residual block 702 larger than a pre-defined value to avoid an invalid model. The threshold representing a measure of computational costs of the GAN. In some examples, the threshold is a number of multiply-accumulate operations (MACs). In some examples, the threshold is a measure of computational latency. It is to be understood that any measure of computational cost may be used as the threshold.
[0106] At operation 806, based on the threshold, the image compression system 130 generates a second GAN by pruning channels of the first GAN. In some examples, the image compression system 130 prunes the channels through the magnitudes of scaling factors in normalization layers, such as Batch Normalization (BN) and Instance Normalization (IN). All channels with scale smaller than the threshold are pruned until the final model achieves the target computation budget. Given a residual block 702, the image compression system 130 may change both the number of channels in each layer and modify the operation, such that, e.g., one residual block 702 may include layers with kernel sizes 1×1 and 3×3. The image compression system 130 prunes channels of the normalization layers together with the corresponding convolution layers. Specifically, the image compression system 130 prunes the first normalization layers for each operation in the residual block 702, namely the ones after the first k×k convolution layers for conventional operations and the ones after the first 1×1 convolution layers for depth-wise operations.
[0107] An example algorithm for searching via the one-step pruning described above is as follows. The algorithm requires a computational budget Tb, a teacher model GT, scaling factors
[0108]
which are used for pruning of the i-th channel in normalization layers N(l)∈GT, a minimum number of output channels clb for convolutional layers outside the residual block. The example algorithm ensures a pruned student architecture GS. Step one is to initialize the scale lower bound as follows:
which are used for pruning of the i-th channel in normalization layers N(l)∈GT, a minimum number of output channels clb for convolutional layers outside the residual block. The example algorithm ensures a pruned student architecture GS. Step one is to initialize the scale lower bound as follows:
[0109]
Step two is to initialize the scale upper bound bas follows:
Step two is to initialize the scale upper bound bas follows:
[0110]
The remaining steps of the algorithm are described using pseudocode below:
The remaining steps of the algorithm are described using pseudocode below:
[0111]
| while ylo < yhi do | |
| Yth ← (Ylo + Yhi)/2 | |
| Prune channels satisfying Yi(l) < Yth on GT while keeping clb to | |
| get GS | |
| T ← computational cost of GS | |
| if T > Tb then | |
| Ylo ← Yth | |
| else | |
| Yhi ← Yth | |
| end if | |
| end while | |
[0112] At operation 808, the image compression system 130 trains the second GAN using similarity-based knowledge distillation from the first GAN. The image compression system 130 transfers knowledge between the two networks' feature spaces. Specifically, to avoid information loss (due to the uneven number of channels between the first GAN and the second GAN), the image compression system 130 encourages similarity between the two feature spaces directly. To compare the similarity between the first GAN and the second GAN, the image compression system 130 computes a similarity metric. The similarity metric discussed herein will be referred to ask the Global-CKA (GCKA). The GCKA is defined as follows:
[0113]
where X, and Y are the same two tensors and where
ρ:
n×hwc→nhw×c is a reshape operation on the input matrix. The GCKA sums feature similarity over channels, characterizing both batch-wise and spatial-wise similarity. The computational complexity of this operation is ϑ(nhw max(c1,c2)2) which is lower than conventional similarity metrics. The image compression system 130 conducts distillation on the features space. For example, if SKD denotes the set of layers for performing knowledge distillation, whereas
where X, and Y are the same two tensors and where
ρ:
n×hwc→nhw×c is a reshape operation on the input matrix. The GCKA sums feature similarity over channels, characterizing both batch-wise and spatial-wise similarity. The computational complexity of this operation is ϑ(nhw max(c1,c2)2) which is lower than conventional similarity metrics. The image compression system 130 conducts distillation on the features space. For example, if SKD denotes the set of layers for performing knowledge distillation, whereas
[0114]
and
and
[0115]
denote feature tensors of layer 1 from the first GAN and second GAN respectively. The image compression system 130 minimizes the distillation loss Ldist as follows:
denote feature tensors of layer 1 from the first GAN and second GAN respectively. The image compression system 130 minimizes the distillation loss Ldist as follows:
[0116]
where the minus sign is introduced to maximize feature similarity between the first and second GANs. The first GAN is trained using original loss functions, which includes an adversarial loss Ladv as follows:
where the minus sign is introduced to maximize feature similarity between the first and second GANs. The first GAN is trained using original loss functions, which includes an adversarial loss Ladv as follows:
[0117]
where x and y denote the input and real images and D and G denote the discriminator and generator respectively. For training the second GAN, the image compression system 130 may use the data generated from the first GAN to perform the paired data and train the second GAN the same as the first GAN with a reconstruction loss Lrecon. The second GAN may thus be a compressed for of the first GAN. In some examples, the overall loss for the second GAN may be described as follows:
where x and y denote the input and real images and D and G denote the discriminator and generator respectively. For training the second GAN, the image compression system 130 may use the data generated from the first GAN to perform the paired data and train the second GAN the same as the first GAN with a reconstruction loss Lrecon. The second GAN may thus be a compressed for of the first GAN. In some examples, the overall loss for the second GAN may be described as follows:
[0118]
[0119] In some examples, the overall loss for the second GAN may be described as follows:
[0120]
where λadv, λrecon, λdist and λfm indicate hyper-parameters that balance the losses.
where λadv, λrecon, λdist and λfm indicate hyper-parameters that balance the losses.
[0121] At operation 810, the image compression system 130 stores the trained second GAN. In some examples the trained second GAN may be deployed on a mobile client device.
Machine Architecture
[0122] FIG. 9 is a diagrammatic representation of the machine 900 within which instructions 910 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 900 to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions 910 may cause the machine 900 to execute any one or more of the methods described herein. The instructions 910 transform the general, non-programmed machine 900 into a particular machine 900 programmed to carry out the described and illustrated functions in the manner described.
[0123] The machine 900 may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 900 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine 900 may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smartphone, a mobile device, a wearable device (e.g., a smartwatch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 910, sequentially or otherwise, that specify actions to be taken by the machine 900. Further, while only a single machine 900 is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions 910 to perform any one or more of the methodologies discussed herein. The machine 900, for example, may comprise the client device 102 or any one of a number of server devices forming part of the messaging server system 108. In some examples, the machine 900 may also comprise both client and server systems, with certain operations of a particular method or algorithm being performed on the server-side and with certain operations of the particular method or algorithm being performed on the client-side.
[0124] The machine 900 may include processors 904, memory 906, and input/output I/O components 902, which may be configured to communicate with each other via a bus 940. In an example, the processors 904 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) Processor, a Complex Instruction Set Computing (CISC) Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 908 and a processor 912 that execute the instructions 910. The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although FIG. 9 shows multiple processors 904, the machine 900 may include a single processor with a single-core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.
[0125] The memory 906 includes a main memory 914, a static memory 916, and a storage unit 918, both accessible to the processors 904 via the bus 940. The main memory 906, the static memory 916, and storage unit 918 store the instructions 910 embodying any one or more of the methodologies or functions described herein. The instructions 910 may also reside, completely or partially, within the main memory 914, within the static memory 916, within machine-readable medium 920 within the storage unit 918, within at least one of the processors 904 (e.g., within the Processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 900.
[0126] The I/O components 902 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 902 that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones may include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 902 may include many other components that are not shown in FIG. 9 . In various examples, the I/O components 902 may include user output components 926 and user input components 928. The user output components 926 may include visual components (e.g., a display such as a plasma display panel (PDP), a light-emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The user input components 928 may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.
[0127] In further examples, the I/O components 902 may include biometric components 930, motion components 932, environmental components 934, or position components 936, among a wide array of other components. For example, the biometric components 930 include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye-tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components 932 include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope).
[0128] The environmental components 934 include, for example, one or cameras (with still image/photograph and video capabilities), illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment.
[0129] With respect to cameras, the client device 102 may have a camera system comprising, for example, front cameras on a front surface of the client device 102 and rear cameras on a rear surface of the client device 102. The front cameras may, for example, be used to capture still images and video of a user of the client device 102 (e.g., “selfies”), which may then be augmented with augmentation data (e.g., filters) described above. The rear cameras may, for example, be used to capture still images and videos in a more traditional camera mode, with these images similarly being augmented with augmentation data. In addition to front and rear cameras, the client device 102 may also include a 3600 camera for capturing 360° photographs and videos.
[0130] Further, the camera system of a client device 102 may include dual rear cameras (e.g., a primary camera as well as a depth-sensing camera), or even triple, quad or penta rear camera configurations on the front and rear sides of the client device 102. These multiple cameras systems may include a wide camera, an ultra-wide camera, a telephoto camera, a macro camera and a depth sensor, for example.
[0131] The position components 936 include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.
[0132] Communication may be implemented using a wide variety of technologies. The I/O components 902 further include communication components 938 operable to couple the machine 900 to a network 922 or devices 924 via respective coupling or connections. For example, the communication components 938 may include a network interface Component or another suitable device to interface with the network 922. In further examples, the communication components 938 may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices 924 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).
[0133] Moreover, the communication components 938 may detect identifiers or include components operable to detect identifiers. For example, the communication components 938 may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components 938, such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.
[0134] The various memories (e.g., main memory 914, static memory 916, and memory of the processors 904) and storage unit 918 may store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions 910), when executed by processors 904, cause various operations to implement the disclosed examples.
[0135] The instructions 910 may be transmitted or received over the network 922, using a transmission medium, via a network interface device (e.g., a network interface component included in the communication components 938) and using any one of several well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions 910 may be transmitted or received using a transmission medium via a coupling (e.g., a peer-to-peer coupling) to the devices 924.
Software Architecture
[0136] FIG. 10 is a block diagram 1000 illustrating a software architecture 1004, which can be installed on any one or more of the devices described herein. The software architecture 1004 is supported by hardware such as a machine 1002 that includes processors 1020, memory 1026, and I/O components 1038. In this example, the software architecture 1004 can be conceptualized as a stack of layers, where each layer provides a particular functionality. The software architecture 1004 includes layers such as an operating system 1012, libraries 1010, frameworks 1008, and applications 1006. Operationally, the applications 1006 invoke API calls 1050 through the software stack and receive messages 1052 in response to the API calls 1050.
[0137] The operating system 1012 manages hardware resources and provides common services. The operating system 1012 includes, for example, a kernel 1014, services 1016, and drivers 1022. The kernel 1014 acts as an abstraction layer between the hardware and the other software layers. For example, the kernel 1014 provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionality. The services 1016 can provide other common services for the other software layers. The drivers 1022 are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers 1022 can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., USB drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth.
[0138] The libraries 1010 provide a common low-level infrastructure used by the applications 1006. The libraries 1010 can include system libraries 1018 (e.g., C standard library) that provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 1010 can include API libraries 1024 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries 1010 can also include a wide variety of other libraries 1028 to provide many other APIs to the applications 1006.
[0139] The frameworks 1008 provide a common high-level infrastructure that is used by the applications 1006. For example, the frameworks 1008 provide various graphical user interface (GUI) functions, high-level resource management, and high-level location services. The frameworks 1008 can provide a broad spectrum of other APIs that can be used by the applications 1006, some of which may be specific to a particular operating system or platform.
[0140] In an example, the applications 1006 may include a home application 1036, a contacts application 1030, a browser application 1032, a book reader application 1034, a location application 1042, a media application 1044, a messaging application 1046, a game application 1048, and a broad assortment of other applications such as a third-party application 1040. The applications 1006 are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications 1006, structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application 1040 (e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application 1040 can invoke the API calls 1050 provided by the operating system 1012 to facilitate functionality described herein.
Glossary
[0141] “Carrier signal” refers to any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such instructions. Instructions may be transmitted or received over a network using a transmission medium via a network interface device.
[0142] “Client device” refers to any machine that interfaces to a communications network to obtain resources from one or more server systems or other client devices. A client device may be, but is not limited to, a mobile phone, desktop computer, laptop, portable digital assistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops, multi-processor systems, microprocessor-based or programmable consumer electronics, game consoles, set-top boxes, or any other communication device that a user may use to access a network.
[0143] “Communication network” refers to one or more portions of a network that may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, a network or a portion of a network may include a wireless or cellular network and the coupling may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other types of cellular or wireless coupling. In this example, the coupling may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology.
[0144] “Component” refers to a device, physical entity, or logic having boundaries defined by function or subroutine calls, branch points, APIs, or other technologies that provide for the partitioning or modularization of particular processing or control functions. Components may be combined via their interfaces with other components to carry out a machine process. A component may be a packaged functional hardware unit designed for use with other components and a part of a program that usually performs a particular function of related functions. Components may constitute either software components (e.g., code embodied on a machine-readable medium) or hardware components. A “hardware component” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various examples, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware components of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware component that operates to perform certain operations as described herein. A hardware component may also be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware component may include dedicated circuitry or logic that is permanently configured to perform certain operations. A hardware component may be a special-purpose processor, such as a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC). A hardware component may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware component may include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware components become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware component mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software), may be driven by cost and time considerations. Accordingly, the phrase “hardware component” (or “hardware-implemented component”) should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering examples in which hardware components are temporarily configured (e.g., programmed), each of the hardware components need not be configured or instantiated at any one instance in time. For example, where a hardware component comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware components) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware component at one instance of time and to constitute a different hardware component at a different instance of time. Hardware components can provide information to, and receive information from, other hardware components. Accordingly, the described hardware components may be regarded as being communicatively coupled. Where multiple hardware components exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware components. In examples in which multiple hardware components are configured or instantiated at different times, communications between such hardware components may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware components have access. For example, one hardware component may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware component may then, at a later time, access the memory device to retrieve and process the stored output. Hardware components may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented components that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented component” refers to a hardware component implemented using one or more processors. Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented components. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an API). The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some examples, the processors or processor-implemented components may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other examples, the processors or processor-implemented components may be distributed across a number of geographic locations.
[0145] “Computer-readable storage medium” refers to both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. The terms “machine-readable medium,” “computer-readable medium” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure.
[0146] “Ephemeral message” refers to a message that is accessible for a time-limited duration. An ephemeral message may be a text, an image, a video and the like. The access time for the ephemeral message may be set by the message sender. Alternatively, the access time may be a default setting or a setting specified by the recipient. Regardless of the setting technique, the message is transitory.
[0147] “Machine storage medium” refers to a single or multiple storage devices and media (e.g., a centralized or distributed database, and associated caches and servers) that store executable instructions, routines and data. The term shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media and device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGA, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks The terms “machine-storage medium,” “device-storage medium,” “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure. The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium.”
[0148] “Non-transitory computer-readable storage medium” refers to a tangible medium that is capable of storing, encoding, or carrying the instructions for execution by a machine.
[0149] “Signal medium” refers to any intangible medium that is capable of storing, encoding, or carrying the instructions for execution by a machine and includes digital or analog communications signals or other intangible media to facilitate communication of software or data. The term “signal medium” shall be taken to include any form of a modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal. The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure.
Claims
Certificate of Correction present
The claims shown below may not include correction changes. Use the corrected PDF for authoritative text.
What is claimed is:
1. A method comprising:
generating a first generative adversarial network (GAN) comprising a first type of convolutional layer, a second type of convolutional layer and a plurality of kernel sizes;
identifying a threshold, the threshold representing a measure of computational costs of the GAN;
based on the threshold, generating a second GAN by pruning channels of the first GAN;
training the second GAN using similarity-based knowledge distillation from the first GAN, the training comprising:
computing a global similarity metric between features of the first GAN and features of the second GAN on channels of the first GAN and channels of the second GAN, the global similarity metric characterizing batch-wise and spatial-wise similarity; and
storing the trained second GAN.
2. The method of claim 1, wherein the first type of convolutional layer is a conventional convolutional layer and the second type of convolutional layer is a depth-wise convolutional layer.
3. The method of claim 1, wherein the threshold is a number of multiply-accumulate operations.
4. The method of claim 1, wherein training the second GAN further comprises:
minimizing a distillation loss on a feature space comprising the first GAN and the second GAN using the computed global similarity metric.
5. The method of claim 1, wherein the first GAN comprises of a plurality of channels and wherein generating the second GAN further comprises:
for each channel of the first GAN:
identifying a weight of the channel;
determining that the weight of the channel is less than the threshold; and
based on the determination, pruning the channel.
6. The method of claim 1, wherein the second GAN is stored on a mobile computing device.
7. The method of claim 1, wherein the second GAN is a compressed form of the first GAN.
8. The method of claim 1, wherein the first GAN is a pre-trained GAN.
9. A system comprising:
a processor; and
a memory storing instructions that, when executed by the processor, configure the system to perform operations comprising:
generating a first generative adversarial network (GAN) comprising a first type of convolutional layer, a second type of convolutional layer and a plurality of kernel sizes;
identifying a threshold, the threshold representing a measure of computational costs of the GAN;
based on the threshold, generating a second GAN by pruning channels of the first GAN;
training the second GAN using similarity-based knowledge distillation from the first GAN, the training comprising:
computing a global similarity metric between features of the first GAN and features of the second GAN on channels of the first GAN and channels of the second GAN, the global similarity metric characterizing batch-wise and spatial-wise similarity; and
storing the trained second GAN.
10. The system of claim 9, wherein the first type of convolutional layer is a conventional convolutional layer and the second type of convolutional layer is a depth-wise convolutional layer.
11. The system of claim 9, wherein the threshold is a number of multiply-accumulate operations.
12. The system of claim 9, wherein training the second GAN further comprises:
minimizing a distillation loss on a feature space comprising the first GAN and the second GAN using the computed global similarity metric.
13. The system of claim 9, wherein the first GAN comprises of a plurality of channels and wherein generating the second GAN further comprises:
for each channel of the first GAN:
identifying a weight of the channel;
determining that the weight of the channel is less than the threshold; and
based on the determination, pruning the channel.
14. The system of claim 9, wherein the second GAN is stored on a mobile computing device.
15. The system of claim 9, wherein the second GAN is a compressed form of the first GAN.
16. The system of claim 9, wherein the first GAN is a pre-trained GAN.
17. A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform operations comprising:
generating a first generative adversarial network (GAN) comprising a first type of convolutional layer, a second type of convolutional layer and a plurality of kernel sizes;
identifying a threshold, the threshold representing a measure of computational costs of the GAN;
based on the threshold, generating a second GAN by pruning channels of the first GAN;
training the second GAN using similarity-based knowledge distillation from the first GAN, the training comprising:
computing a global similarity metric between features of the first GAN and features of the second GAN on channels of the first GAN and channels of the second GAN, the global similarity metric characterizing batch-wise and spatial-wise similarity; and
storing the trained second GAN.
18. The computer-readable storage medium of claim 17, wherein the first type of convolutional layer is a conventional convolutional layer and the second type of convolutional layer is a depth-wise convolutional layer.
19. The computer-readable storage medium of claim 17, wherein the threshold is a number of multiply-accumulate operations.
20. The computer-readable storage medium of claim 17, wherein training the second GAN further comprises:
minimizing a distillation loss on a feature space comprising the first GAN and the second GAN using the computed global similarity metric.
Patent Citations (9)
| Patent | Date | Inventor | Cited By |
|---|---|---|---|
| US10984245(B1) | 2021-04-01 | Tran | Examiner |
| US11397894(B2) | 2022-07-01 | Cho | Examiner |
| US2019/0050735(A1) | 2019-02-01 | Ji | Examiner |
| US2021/0264278(A1) | 2021-08-01 | Liu | Examiner |
| US2022/0004803(A1) | 2022-01-01 | Li | Examiner |
| CN111191667 | 2020-05-01 | Applicant | |
| CN112052948 | 2020-12-01 | Applicant | |
| CN116685981(A) | 2023-09-01 | Applicant | |
| WO2022146799 | 2022-07-01 | Applicant |
Non-Patent Literature (97)
- Wang, Haotao, et al. “Gan slimming: All-in-one gan compression by a unified optimization framework.” European Conference on Computer Vision. Cham: Springer International Publishing, 2020. (Year: 2020).Examiner
- Wang, Ting-Chun, et al., “High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs”, [Online]. Retrieved from the Internet: <URL: https://arxiv.org/pdf/1711.11585.pdf>, (Aug. 20, 2018), 14 pgs.Applicant
- “International Application Serial No. PCT/US2021/064717, International Search Report mailed Apr. 12, 2022”, 3 pgs.Applicant
- “International Application Serial No. PCT/US2021/064717, Written Opinion mailed Apr. 12, 2022”, 5 pgs.Applicant
- Li, Muyang, “GAN Compression: Efficient Architectures for Interactive Conditional GANs”, arxiv.org, Cornell University Library, 201 Olin Library Cornell University Ithaca, NY 14853, (Dec. 16, 2020), 22 pgs.Applicant
- “Chinese Application Serial No. 202180088244.8, Voluntary Amendment filed Dec. 1, 2023”, w/ English machine translation, 59 pgs.Applicant
- “International Application Serial No. PCT/US2021/064717, International Preliminary Report on Patentability mailed Jul. 13, 2023”, 7 pgs.Applicant
- Aguinaldo, Angeline, “Compressing GANs using Knowledge Distillation”, Arxiv.Org, Cornell University Library, 201 Olin Library Cornell University Ithaca, Ny 14853, (Feb. 28, 2019), 10 pgs.Applicant
- Binkowski, Mikolaj, et al., “Demystifying mmd gans”, (2018), 36 pgs.Applicant
- Bolukbasi, Tolga, et al., “Adaptive neural networks for efficient inference”, arXiv preprint arXiv:1702.07811, (2017), 10 pgs.Applicant
- Brock, Andrew, et al., “Large scale gan training for high fidelity natural image synthesis”, arXiv preprint arXiv:1809.11096, (2018), 35 pgs.Applicant
- Cai, Han, et al., “Once-for-all: Train one network and specialize it for efficient deployment”, arXiv preprint arXiv:1908.09791, (2020), 15 pgs.Applicant
- Cai, Han, et al., “Proxylessnas: Direct neural architecture search on target task and hardware”, arXiv preprint arXiv:1812.00332, (2019), 13 pgs.Applicant
- Chai, Menglei, et al., “Neural Hair Rendering”, Arxiv.Org, Cornell University Library, 201 Olin Library Cornell University Ithaca, NY 14853, (Jul. 21, 2020), 18 pgs.Applicant
- Chen, Guobin, et al., “Learning Efficient Object Detection Models with Knowledge Distillation”, 31st Conference on Neural Information Processing Systems, (2017), 10 pgs.Applicant
- Chen, Hanting, et al., “Distilling portable generative adversarial networks for image translation”, arXiv preprint arXiv:2003.03519, (Mar. 7, 2020), 9 pgs.Applicant
- Chen, Xin, et al., “Progressive differentiable architecture search: Bridging the depth gap between search and evaluation”, In Proceedings of the IEEE International Conference on Computer Vision, (2019), 10 pgs.Applicant
- Chen, Xuxi, et al., “Gans Can Play Lottery Tickets Too”, In Submitted to International Conference on Learning Representations, under review, (May 31, 2021), 16 pgs.Applicant
- Choi, Jungwook, et al., “Pact: Parameterized clipping activation for quantized neural networks”, arXiv preprint arXiv:1805.06085, (Jul. 17, 2018), 15 pgs.Applicant
- Cordts, Marius, et al., “The Cityscapes Dataset for Semantic Urban Scene Understanding”, IEEE Conference on Computer Vision and Pattern Recognition, (2016), 11 pgs.Applicant
- Cortes, Corinna, et al., “Algorithms for learning kernels based on centered alignment”, The Journal of Machine Learning Research, 13(1), pp. 795-828, (2012), 35 pgs.Applicant
- Cristianini, Nello, et al., “On Kernel-Target Alignment”, In Innovations in machine learning, Springer, pp. 205-256, (2006), 7 pgs.Applicant
- Ding, Caiwen, et al., “Circnn: accelerating and compressing deep neural networks using block-circulant weight matrices”, In Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture, pp. 395-408, (Aug. 29, 2017), 14 pgs.Applicant
- Ding, Caiwen, et al., “Req-Yolo: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs”, In Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, (Sep. 29, 2019), 11 pgs.Applicant
- Ding, Caiwen, et al., “Structured weight matrices-based hardware accelerators in deep neural networks: Fpgas and asics”, In Proceedings of the 2018 on Great Lakes Symposium on VLSI, (Mar. 28, 2018), 6 pgs.Applicant
- Frankle, Jonathan, et al., “The lottery ticket hypothesis: Finding sparse, trainable neural networks”, arXiv preprint arXiv:1803.03635, (Mar. 4, 2019), 42 pgs.Applicant
- Fu, Yonggan, et al., “Autogan-distiller: Searching to compress generative adversarial networks”, arXiv preprint arXiv:2006.08198, (2020), 12 pgs.Applicant
- Goodfellow, Ian J, et al., “Generative Adversarial Nets”, Proceedings of NIPS, In Advances in neural information processing systems, pp. 2672-2680, (2014), 9 pgs.Applicant
- Gou, Jianping, et al., “Knowledge distillation: A survey”, arXiv preprint arXiv:2006.05525, (May 20, 2021), 36 pgs.Applicant
- Guo, Zichao, et al., “Single path one-shot neural architecture search with uniform sampling”, In European Conference on Computer Vision, Springer, (Jul. 8, 2020), 16 pgs.Applicant
- Han, Shu, et al., “Co-evolutionary compression for unpaired image translation”, In Int. Conf. Comput Vis, (Jul. 25, 2019), 9 pgs.Applicant
- Han, Song, et al., “Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding”, arXiv preprint, arXiv:1510.00149v5, (Jan. 19, 2016), 14 pgs.Applicant
- Heusel, Martin, et al., “Gans Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium”, Advances in neural information processing systems, 30, (2017), 12 pgs.Applicant
- Hinton, Geoffrey, et al., “Distilling the Knowledge in a Neural Network”, arXiv:1503.02531v1 [stat.ML], (2015), 9 pgs.Applicant
- Howard, Andrew G., et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”, arXiv:1704.04861v1, (Apr. 17, 2017), 9 pgs.Applicant
- Ioffe, Sergey, et al., “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift”, arXiv preprint, arXiv: 1502.03167v3 [cs.LG], (Mar. 2, 2015), 11 pgs.Applicant
- Isola, Phillip, et al., “Image-to-Image Translation with Conditional Adversarial Networks”, arXiv:1611.07004 [cs.CV], (Nov. 22, 2017), 17 pgs.Applicant
- Jieru, Mei, et al., “Atomnas: Finegrained end-to-end neural architecture search”, arXiv preprint arXiv:1912.09640, (2019), 14 pgs.Applicant
- Jin, Qing, et al., “Adabits: Neural network quantization with adaptive bit-widths”, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (Mar. 15, 2020), 12 pgs.Applicant
- Jin, Qing, et al., “Neural network quantization with scale-adjusted training”, In The British Machine Vision Conference (BMVC), (2020), 14 pgs.Applicant
- Jin, Qing, et al., “Teachers Do More Than Teach: Compressing Image-to-Image Models”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13600-13611, (2021), 12 pgs.Applicant
- Jin, Qing, et al., “Towards efficient training for neural network quantization”, arXiv preprint arXiv:1912.10207, (Dec. 21, 2019), 17 pgs.Applicant
- Karras, Tero, et al., “Analyzing and improving the image quality of stylegan”, Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, (2020), 10 pgs.Applicant
- Kornblith, Simon, et al., “Similarity of neural network representations revisited”, arXiv preprint arXiv:1905.00414, (Jul. 19, 2019), 20 pgs.Applicant
- Li, Hongjia, et al., “Admm-based weight pruning for real-time deep learning acceleration on mobile devices”, In Proceedings of the 2019 on Great Lakes Symposium on VLSI, (2019), 6 pgs.Applicant
- Li, Yingwei, et al., “Neural architecture search for lightweight non-local networks”, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10297-10306, (2020), 10 pgs.Applicant
- Lin, Sheng, et al., “Toward extremely low bit and lossless accuracy in dnns with progressive admm”, arXiv preprint arXiv:1905.00789, (May 2, 2019), 4 pgs.Applicant
- Liu, Hanxiao, et al., “Darts: Differentiable architecture search”, arXiv preprint arXiv: 1806.09055, (Apr. 23, 2019), 13 pgs.Applicant
- Liu, Ning, et al., “AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates”, In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 4876-4883, (2020), 8 pgs.Applicant
- Liu, Ning, et al., “Autoslim: An automatic dnn structured pruning framework for ultra-high compression rates”, arXiv preprint arXiv: 1907.03141, (Sep. 11, 2019), 10 pgs.Applicant
- Liu, Shaoshan, et al., “Cocopie: Making mobile ai sweet as pie-compressioncompilation co-design goes a long way”, arXiv preprint arXiv:2003.06700, (2020), 25 pgs.Applicant
- Liu, Zhuang, et al., “Learning Efficient Convolutional Networks through Network Slimming”, Proceedings of the IEEE international conference on computer vision, (2017), 2755-2763.Applicant
- Liu, Zhuang, et al., “Rethinking the Value of Network Pruning”, arXiv:1810.05270v2, (Mar. 5, 2019), 21 pgs.Applicant
- Lopez-Paz, David, et al., “Unifying distillation and privileged information.”, arXiv preprint arXiv:1511.03643, (2015), 10 pgs.Applicant
- Lu, Zhichao, et al., “Neural architecture transfer”, arXiv preprint arXiv:2005.05859, (2020), 22 pgs.Applicant
- Lu, Zhichao, et al., “Nsganetv2: Evolutionary multi-objective surrogate-assisted neural architecture search”, In European Conference on Computer Vision, pp. 35-51, (2020), 22 pgs.Applicant
- Luo, Ping, et al., “Face model compression by distilling knowledge from neurons”, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), (2016), 3560-3566.Applicant
- Ma, Xiaolong, et al., “An image enhancing pattern-based sparsity for real-time inference on mobile devices”, In European Conference on Computer Vision Springer, 629-645, (2020), 17 pgs.Applicant
- Ma, Xiaolong, et al., “Non-structured dnn weight pruning-is it beneficial in any platform?”, Journal of Latex Class Files, vol. 14, No. 8, Aug. 2015, (2019), 15 pgs.Applicant
- Ma, Xiaolong, et al., “Pconv: The missing but desirable sparsity in dnn weight pruning for realtime execution on mobile devices”, In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5117-5124, (2020), 8 pgs.Applicant
- Ma, Xiaolong, et al., “Resnet can be pruned 60X: Introducing network purification and unused path removal (p-rm) after weight pruning”, In 2019 IEEE/ACM International Symposium on Nanoscale Architectures (Nanoarch) IEEE, (Apr. 30, 2019), 4 pgs.Applicant
- Mirza, Mehdi, et al., “Conditional generative adversarial nets”, arXiv preprint arXiv:1411.1784, (2014), 7 pgs.Applicant
- Real, Esteban, et al., “Large-scale evolution of image classifiers”, arXiv preprint arXiv:1703.01041, (Jun. 11, 2017), 18 pgs.Applicant
- Ren, Jian, et al., “Eigen: Ecologically-inspired genetic approach for neural network structure searching from scratch”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (Apr. 12, 2019), 10 pgs.Applicant
- Ren, Jian, et al., “Human Motion Transfer from Poses in the Wild”, arXiv:2004.03142v1, (Apr. 7, 2020), 18 pgs.Applicant
- Sandler, Mark, et al., “MobileNetV2: Inverted Residuals and Linear Bottlenecks”, arXiv preprint arXiv:1801.04381v4 [cs.CV], (Mar. 21, 2019), 14 pgs.Applicant
- Shi, Runbin, et al., “Csb-rnn: A faster-than-realtime rnn acceleration framework with compressed structured blocks”, arXiv preprint arXiv:2005.05758, (May 11, 2020), 12 pgs.Applicant
- Szegedy, Christian, et al., “Inception-v4, inception-resnet and the impact of residual connections on learning”, arXiv preprint arXiv:1602.07261, (Aug. 23, 2016), 12 pgs.Applicant
- Taesung, Park, et al., “Semantic image synthesis with spatially-adaptive normalization”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (Nov. 5, 2019), 19 pgs.Applicant
- Takeru, Miyato, et al., “Spectral normalization for generative adversarial networks”, arXiv preprint arXiv:1802.05957, (2018), 26 pgs.Applicant
- Tan, Mingxing, et al., “Mnasnet: Platform-aware neural architecture search for mobile”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2019), 2820-2828.Applicant
- Tan, Zhentao, et al., “Rethinking Spatially-Adaptive Normalization”, arXiv preprint arXiv:2004.02867, (2020), 23 pgs.Applicant
- Tian, Yu, et al., “A good image generator is what you need for high-resolution video synthesis”, In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria,, (2021), 23 pgs.Applicant
- Ulyanov, Dmitry, et al., “Instance Normalization: The Missing Ingredient for Fast Stylization”, arXiv preprint, arXiv:1607.08022v3 [cs.CV], (Nov. 6, 2017), 6 pgs.Applicant
- Wang, Ting-Chun, et al., “Video-to-Video Synthesis”, arXiv preprint arXiv:1808.06601, (2018).Applicant
- Wang, Xin, et al., “SkipNet: Learning Dynamic Routing in Convolutional Networks”, In Proceedings of the European Conference on Computer Vision (ECCV), 409-424, (2018), 19 pgs.Applicant
- Wei, Niu, et al., “26ms inference time for resnet-50: Towards real-time execution of all dnns on smartphone”, arXiv preprint arXiv: 1905.00571, (May 2, 2019), 4 pgs.Applicant
- Wei, Niu, et al., “Patdnn: Achieving real-time dnn execution on mobile devices with pattern-based weight pruning”, In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, 907-922, (Jan. 22, 2020), 16 pgs.Applicant
- Wu, Bichen, et al., “FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search”, In Proc. IEEE/CVF Conf. Computer Vision Pattern Recognition (CVPR), (2018), 10726-10734.Applicant
- Yang, Linjie, et al., “FracBits: Mixed Precision Quantization via Fractional Bit-Widths”, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, (2020), 9 pgs.Applicant
- Yim, Junho, et al., “A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2017), 4133-4141.Applicant
- Yu, Fisher, et al., “Dilated Residual Networks”, In Proceedings of the IEEE conference on computer vision and pattern recognition, (2017), 472-480.Applicant
- Yu, Jiahui, et al., “AutoSlim: Towards One-Shot Architecture Search for Channel Numbers”, arXiv preprint arXiv:1903.11728, (2019), 10 pgs.Applicant
- Yu, Jiahui, et al., “BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models”, arXiv preprint arXiv:2003.11142, (2020), 21 pgs.Applicant
- Yu, Jiahui, et al., “Universally Slimmable Networks and Improved Training Techniques”, In Proceedings of the IEEE International Conference on Computer Vision, (2019), 1803-1811.Applicant
- Yu, Qihang, et al., “Cakes: Channel-wise automatic kernel shrinking for efficient 3d network”, arXiv preprint arXiv:2003.12798, (2020), 9 pgs.Applicant
- Yuan, Geng, et al., “An ultra-efficient memristor-based dnn framework with structured weight pruning and quantization using admm”, In 2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), (Aug. 29, 2019), 6 pgs.Applicant
- Zhang, Han, et al., “Self-attention generative adversarial networks”, In International Conference on Machine Learning, 7354-7363. PMLR, (Jun. 14, 2019), 10 pgs.Applicant
- Zhang, Tianyun, et al., “StructADMM: Achieving Ultrahigh Efficiency in Structured Pruning for DNNs”, IEEE Transactions on Neural Networks and Learning Systems, vol. 33, No. 5, (May 2022), 2259-2273.Applicant
- Zhu, Jun-Yan, et al., “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”, IEEE International Conference on Computer Vision, (2017), 2223-2232.Applicant
- Zoph, Barret, et al., “Neural architecture search with reinforcement learning”, arXiv preprint arXiv:1611.01578, (2016), 15 pgs.Applicant
- Zoph, Barrett, et al., “Learning transferable architectures for scalable image recognition”, In Proceedings of the IEEE conference on computer vision and pattern recognition, 8697-8710, (Apr. 11, 2018), 14 pgs.Applicant
- “Chinese Application Serial No. 202180088244.8, Office Action mailed Dec. 22, 2025”, w/ English Translation, 24 pgs.Applicant
- “European Application Serial No. 21847617.4, Communication Pursuant to Article 94(3) EPC mailed Jan. 14, 2026”, 17 pgs.Applicant
- Wu, Junde, “Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning”, arXiv:2007.11355v1 [cs.CV], (Jul. 22, 2020), 10 pgs.Applicant
- “Korean Application Serial No. 10-2023-7025391, Notice of Preliminary Rejection mailed Jan. 30, 2026”, W/ English Translation, 11 pgs.Applicant
- Li, Muyang, et al., “Gan compression: Efficient architectures for interactive conditional gans”, In IEEE Conf. Comput. Vis. Pattern Recog. pp. 5284-5294, (2020), 11 pgs.Applicant