公开(公告)号：US11829880B2

公开(公告)日：2023-11-28

申请号：US18049209

申请日：2022-10-24

Applicant: Adobe Inc.

Inventor： Mayank Singh ,  Nupur Kumari ,  Dhruv Khattar ,  Balaji Krishnamurthy ,  Abhishek Sinha

IPC: G06N3/08 ,  G06N20/00 ,  H04L9/40

CPC classification number: G06N3/08 ,  G06N20/00 ,  H04L63/1441

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating trained neural network with increased robustness against adversarial attacks by utilizing a dynamic dropout routine and/or a cyclic learning rate routine. For example, the disclosed systems can determine a dynamic dropout probability distribution associated with neurons of a neural network. The disclosed systems can further drop neurons from a neural network based on the dynamic dropout probability distribution to help neurons learn distinguishable features. In addition, the disclosed systems can utilize a cyclic learning rate routine to force copy weights of a copy neural network away from weights of an original neural network without decreasing prediction accuracy to ensure that the decision boundaries learned are different.

公开(公告)号：US20230316379A1

公开(公告)日：2023-10-05

申请号：US18186528

申请日：2023-03-20

Applicant: Adobe Inc.

Inventor： Kumar AYUSH ,  Ayush Chopra ,  Patel U. Govind ,  Balaji Krishnamurthy ,  Anirudh Singhal

IPC: G06Q30/0601 ,  G06N3/088 ,  G06F18/214 ,  G06F18/21 ,  G06N3/045 ,  G06V10/764 ,  G06V10/82 ,  G06V10/44 ,  G06V20/00

CPC classification number: G06Q30/0631 ,  G06F18/214 ,  G06F18/2193 ,  G06N3/045 ,  G06N3/088 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82 ,  G06V20/00

Abstract: Systems, methods, and computer storage media are disclosed for predicting visual compatibility between a bundle of catalog items (e.g., a partial outfit) and a candidate catalog item to add to the bundle. Visual compatibility prediction may be jointly conditioned on item type, context, and style by determining a first compatibility score jointly conditioned on type (e.g., category) and context, determining a second compatibility score conditioned on outfit style, and combining the first and second compatibility scores into a unified visual compatibility score. A unified visual compatibility score may be determined for each of a plurality of candidate items, and the candidate item with the highest unified visual compatibility score may be selected to add to the bundle (e.g., fill the in blank for the partial outfit).

公开(公告)号：US20230267663A1

公开(公告)日：2023-08-24

申请号：US17678237

申请日：2022-02-23

Applicant: Adobe Inc.

Inventor： Ayush Chopra ,  Rishabh Jain ,  Mayur Hemani ,  Balaji Krishnamurthy

IPC: G06T11/60 ,  G06T7/70 ,  G06T7/11 ,  G06N3/04

CPC classification number: G06T11/60 ,  G06T7/70 ,  G06T7/11 ,  G06N3/0454

Abstract: In implementations of systems for generating images for virtual try-on and pose transfer, a computing device implements a generator system to receive input data describing a first digital image that depicts a person in a pose and a second digital image that depicts a garment. Candidate appearance flow maps are computed that warp the garment based on the pose at different pixel-block sizes using a first machine learning model. The generator system generates a warped garment image by combining the candidate appearance flow maps as an aggregate per-pixel displacement map using a convolutional gated recurrent network. A conditional segment mask is predicted that segments portions of a geometry of the person using a second machine learning model. The generator system outputs a digital image that depicts the person in the pose wearing the garment based on the warped garment image and the conditional segmentation mask using a third machine learning model.

公开(公告)号：US11645541B2

公开(公告)日：2023-05-09

申请号：US15815899

申请日：2017-11-17

Applicant: Adobe Inc.

Inventor： Piyush Gupta ,  Nikaash Puri ,  Balaji Krishnamurthy

IPC: G06N3/086 ,  G06N20/00 ,  G06F16/35 ,  G06N3/126 ,  G06N3/08 ,  G06N5/045 ,  G06N5/025 ,  G06N5/01 ,  G06N20/20

CPC classification number: G06N3/086 ,  G06F16/353 ,  G06N3/08 ,  G06N3/126 ,  G06N5/045 ,  G06N20/00 ,  G06N5/01 ,  G06N5/025 ,  G06N20/20

Abstract: A technique is disclosed for generating class level rules that globally explain the behavior of a machine learning model, such as a model that has been used to solve a classification problem. Each class level rule represents a logical conditional statement that, when the statement holds true for one or more instances of a particular class, predicts that the respective instances are members of the particular class. Collectively, these rules represent the pattern followed by the machine learning model. The techniques are model agnostic, and explain model behavior in a relatively easy to understand manner by outputting a set of logical rules that can be readily parsed. Although the techniques can be applied to any number of applications, in some embodiments, the techniques are suitable for interpreting models that perform the task of classification. Other machine learning model applications can equally benefit.

公开(公告)号：US11640634B2

公开(公告)日：2023-05-02

申请号：US16865572

申请日：2020-05-04

Applicant: ADOBE INC.

Inventor： Kumar Ayush ,  Ayush Chopra ,  Patel Utkarsh Govind ,  Balaji Krishnamurthy ,  Anirudh Singhal

IPC: G06N3/00 ,  G06N3/088 ,  G06N3/04 ,  G06K9/62 ,  G06Q30/0601

Abstract: Systems, methods, and computer storage media are disclosed for predicting visual compatibility between a bundle of catalog items (e.g., a partial outfit) and a candidate catalog item to add to the bundle. Visual compatibility prediction may be jointly conditioned on item type, context, and style by determining a first compatibility score jointly conditioned on type (e.g., category) and context, determining a second compatibility score conditioned on outfit style, and combining the first and second compatibility scores into a unified visual compatibility score. A unified visual compatibility score may be determined for each of a plurality of candidate items, and the candidate item with the highest unified visual compatibility score may be selected to add to the bundle (e.g., fill the in blank for the partial outfit).

公开(公告)号：US11631205B2

公开(公告)日：2023-04-18

申请号：US17657255

申请日：2022-03-30

Applicant: Adobe Inc.

Inventor： Nupur Kumari ,  Piyush Gupta ,  Akash Rupela ,  Siddarth R ,  Balaji Krishnamurthy

IPC: G06T11/20 ,  G06F17/11

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate interactive visual shape representation of digital datasets. For example, the disclosed systems can generate an augmented nearest neighbor network graph from a sampled subset of digital data points using a nearest neighbor model and witness complex model. The disclosed system can further generate a landmark network graph based on the augmented nearest neighbor network graph utilizing a plurality of random walks. The disclosed systems can also generate a loop-augmented spanning network graph based on a partition of the landmark network graph by adding community edges between communities of landmark groups based on modularity and to complete community loops. Based on the loop-augmented spanning network graph, the disclosed systems can generate an interactive visual shape representation for display on a client device.

公开(公告)号：US20220245141A1

公开(公告)日：2022-08-04

申请号：US17656772

申请日：2022-03-28

Applicant: Adobe Inc.

Inventor： Milan Aggarwal ,  Balaji Krishnamurthy

IPC: G06F16/242 ,  G06N20/00 ,  G06F16/248

Abstract: An interactive search session is implemented using an artificial intelligence model. For example, when the artificial intelligence model receives a search query from a user, the model selects an action from a plurality of actions based on the search query. The selected action queries the user for more contextual cues about the search query (e.g., may enquire about use of the search results, may request to refine the search query, or otherwise engage the user in conversation to better understand the intent of the search). The interactive search session may be in the form, for example, of a chat session between the user and the system, and the chat session may be displayed along with the search results (e.g., in a separate section of display). The interactive search session may enable the system to better understand the user's search needs, and accordingly may help provide more focused search results.

公开(公告)号：US11294891B2

公开(公告)日：2022-04-05

申请号：US16394853

申请日：2019-04-25

Applicant: Adobe Inc.

Inventor： Milan Aggarwal ,  Balaji Krishnamurthy

IPC: G06F16/332 ,  G06F16/242 ,  G06N20/00 ,  H04L51/02 ,  G06F40/205

Abstract: Techniques are disclosed for providing an interactive search session. The interactive search session is implemented using an artificial intelligence model. For example, when the artificial intelligence model receives a search query from a user, the model selects an action from a plurality of actions based on the search query. The selected action queries the user for more contextual cues about the search query (e.g., may enquire about use of the search results, may request to refine the search query, or otherwise engage the user in conversation to better understand the intent of the search). The interactive search session may be in the form, for example, of a chat session between the user and the system, and the chat session may be displayed along with the search results (e.g., in a separate section of display). The interactive search session may enable the system to better understand the user's search needs, and accordingly may help provide more focused search results.

公开(公告)号：US20220012530A1

公开(公告)日：2022-01-13

申请号：US16926511

申请日：2020-07-10

Applicant: Adobe Inc.

Inventor： Mayank SINGH ,  Balaji Krishnamurthy ,  Nupur KUMARI ,  Puneet MANGLA

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06T7/11

Abstract: Embodiments are disclosed for training a neural network classifier to learn to more closely align an input image with its attribution map. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving a training image comprising a representation of one or more objects, the training image associated with at least one label for the representation of the one or more objects, generating a perturbed training image based on the training image using a neural network, and training the neural network using the perturbed training image by minimizing a combination of classification loss and attribution loss to learn to align an image with its corresponding attribution map.

公开(公告)号：US20210327108A1

公开(公告)日：2021-10-21

申请号：US16850677

申请日：2020-04-16

Applicant: Adobe Inc.

Inventor： Nupur Kumari ,  Piyush Gupta ,  Akash Rupela ,  Siddarth R ,  Balaji Krishnamurthy

IPC: G06T11/20 ,  G06F17/11

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate interactive visual shape representation of digital datasets. For example, the disclosed systems can generate an augmented nearest neighbor network graph from a sampled subset of digital data points using a nearest neighbor model and witness complex model. The disclosed system can further generate a landmark network graph based on the augmented nearest neighbor network graph utilizing a plurality of random walks. The disclosed systems can also generate a loop-augmented spanning network graph based on a partition of the landmark network graph by adding community edges between communities of landmark groups based on modularity and to complete community loops. Based on the loop-augmented spanning network graph, the disclosed systems can generate an interactive visual shape representation for display on a client device.