公开(公告)号：US20210349915A1

公开(公告)日：2021-11-11

申请号：US17383009

申请日：2021-07-22

Applicant: Adobe Inc.

Inventor： Akash Rupela ,  Piyush Gupta ,  Nupur Kumari ,  Bishal Deb ,  Balaji Krishnamurthy ,  Ankita Sarkar

IPC: G06F16/26 ,  G06F16/22 ,  G06F16/28 ,  G06F16/248 ,  G06F3/0481 ,  G06K9/62

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that generate and render a varied-scale-topological construct for a multidimensional dataset to visually represent portions of the multidimensional dataset at different topological scales. In certain implementations, for example, the disclosed systems generate and combine (i) an initial topological construct for a multidimensional dataset at one scale and (ii) a local topological construct for a subset of the multidimensional dataset at another scale to form a varied-scale-topological construct. To identify a region from an initial topological construct to vary in scale, the disclosed systems can determine the relative densities of subsets of multidimensional data corresponding to regions of the initial topological construct and select one or more such regions to change in scale.

公开(公告)号：US11100127B2

公开(公告)日：2021-08-24

申请号：US16368415

申请日：2019-03-28

Applicant: Adobe Inc.

Inventor： Akash Rupela ,  Piyush Gupta ,  Nupur Kumari ,  Bishal Deb ,  Balaji Krishnamurthy ,  Ankita Sarkar

IPC: G06F16/26 ,  G06F16/22 ,  G06F16/28 ,  G06F16/248 ,  G06F3/0481 ,  G06K9/62

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that generate and render a varied-scale-topological construct for a multidimensional dataset to visually represent portions of the multidimensional dataset at different topological scales. In certain implementations, for example, the disclosed systems generate and combine (i) an initial topological construct for a multidimensional dataset at one scale and (ii) a local topological construct for a subset of the multidimensional dataset at another scale to form a varied-scale-topological construct. To identify a region from an initial topological construct to vary in scale, the disclosed systems can determine the relative densities of subsets of multidimensional data corresponding to regions of the initial topological construct and select one or more such regions to change in scale.

公开(公告)号：US20210256387A1

公开(公告)日：2021-08-19

申请号：US16793551

申请日：2020-02-18

Applicant: Adobe Inc.

Inventor： Ayush Chopra ,  Balaji Krishnamurthy ,  Mausoom Sarkar ,  Surgan Jandial

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62

Abstract: Generating a machine learning model that is trained using retrospective loss is described. A retrospective loss system receives an untrained machine learning model and a task for training the model. The retrospective loss system initially trains the model over warm-up iterations using task-specific loss that is determined based on a difference between predictions output by the model during training on input data and a ground truth dataset for the input data. Following the warm-up training iterations, the retrospective loss system continues to train the model using retrospective loss, which is model-agnostic and constrains the model such that a subsequently output prediction is more similar to the ground truth dataset than the previously output prediction. After determining that the model's outputs are within a threshold similarity to the ground truth dataset, the model is output with its current parameters as a trained model.

公开(公告)号：US11017016B2

公开(公告)日：2021-05-25

申请号：US15940849

申请日：2018-03-29

Applicant: Adobe Inc.

Inventor： Vikas Yadav ,  Balaji Krishnamurthy ,  Mausoom Sarkar ,  Rajiv Mangla ,  Gitesh Malik

IPC: G06F16/58 ,  G06F16/583 ,  G06F16/783

Abstract: A method for clustering product media files is provided. The method includes dividing each media file corresponding to one or more products into a plurality of tiles. The media file include one of an image or a video. Feature vectors are computed for each tile of each media file. One or more patch clusters are generated using the plurality of tiles. Each patch cluster includes tiles having feature vectors similar to each other. The feature vectors of each media file are compared with feature vectors of each patch cluster. Based on comparison, product groups are then generated. All media files having comparison output similar to each other are grouped into one product group. Each product group includes one or more media files for one product. Apparatus for substantially performing the method as described herein is also provided.

公开(公告)号：US20210142539A1

公开(公告)日：2021-05-13

申请号：US16679165

申请日：2019-11-09

Applicant: Adobe Inc.

Inventor： Kumar Ayush ,  Surgan Jandial ,  Abhijeet Kumar ,  Mayur Hemani ,  Balaji Krishnamurthy ,  Ayush Chopra

IPC: G06T11/40 ,  G06T3/00 ,  G06N3/04 ,  G06N3/08

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a virtual try-on digital image utilizing a unified neural network framework. For example, the disclosed systems can utilize a coarse-to-fine warping process to generate a warped version of a product digital image to fit a model digital image. In addition, the disclosed systems can utilize a texture transfer process to generate a corrected segmentation mask indicating portions of a model digital image to replace with a warped product digital image. The disclosed systems can further generate a virtual try-on digital image based on a warped product digital image, a model digital image, and a corrected segmentation mask. In some embodiments, the disclosed systems can train one or more neural networks to generate accurate outputs for various stages of generating a virtual try-on digital image.

公开(公告)号：US20210073671A1

公开(公告)日：2021-03-11

申请号：US16564531

申请日：2019-09-09

Applicant: Adobe, Inc.

Inventor： Nikaash Puri ,  Balaji Krishnamurthy ,  Ayush Chopra

IPC: G06N20/00 ,  G06N5/04

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for generating combined feature embeddings for minority class upsampling in training machine learning models with imbalanced training samples. For example, the disclosed systems can select training sample values from a set of training samples and a combination ratio value from a continuous probability distribution. Additionally, the disclosed systems can generate a combined synthetic training sample value by modifying the selected training sample values using the combination ratio value and combining the modified training sample values. Moreover, the disclosed systems can generate a combined synthetic ground truth label based on the combination ratio value. In addition, the disclosed systems can utilize the combined synthetic training sample value and the combined synthetic ground truth label to generate a combined synthetic training sample and utilize the combined synthetic training sample to train a machine learning model.

公开(公告)号：US10902322B2

公开(公告)日：2021-01-26

申请号：US15660547

申请日：2017-07-26

Applicant: Adobe Inc.

Inventor： Shagun Sodhani ,  Balaji Krishnamurthy

IPC: G06N3/08 ,  G06N20/00 ,  G06N20/10 ,  G06N3/04

Abstract: A standardized data model (“SDM”) includes standardized data types that indicate classifications of data elements. In a data service platform, such as a marketing data platform, a data standardization module classifies received data elements. One or more components included in the data standardization module are trained using supervised or unsupervised learning techniques to classify received data elements into a standardized data type included in the SDM. In some cases, an output of an unsupervised learning phase is provided as an input to a supervised learning phase. In some cases, a classified data element is modified by the data standardization module to indicate the standardized data type into which the data element is classified.

公开(公告)号：US20200320112A1

公开(公告)日：2020-10-08

申请号：US16377424

申请日：2019-04-08

Applicant: Adobe Inc.

Inventor： Dheeraj Bansal ,  Sukriti Verma ,  Pratiksha Agarwal ,  Piyush Gupta ,  Nikaash Puri ,  Vishal Wani ,  Balaji Krishnamurthy

IPC: G06F16/332 ,  G06F16/58 ,  G06N3/08 ,  G06N20/20 ,  G06F16/535

Abstract: Systems and methods are described for serving personalized content using content tagging and transfer learning. The method may include identifying content elements in an experience pool, where each of the content element is associated with one or more attribute tags, identifying a user profile comprising characteristics of a user, generating a set of user-tag affinity vectors based on the user profile and the corresponding attribute tags using a content personalization engine, generating a user-content affinity score based on the set of user-tag affinity vectors, selecting a content element from the plurality of content elements based on the corresponding user-content affinity score, and delivering the selected content element to the user.

公开(公告)号：US20200311100A1

公开(公告)日：2020-10-01

申请号：US16368415

申请日：2019-03-28

Applicant: Adobe Inc.

Inventor： Akash Rupela ,  Piyush Gupta ,  Nupur Kumari ,  Bishal Deb ,  Balaji Krishnamurthy ,  Ankita Sarkar

IPC: G06F16/26 ,  G06F16/248 ,  G06F16/28 ,  G06F16/22

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that generate and render a varied-scale-topological construct for a multidimensional dataset to visually represent portions of the multidimensional dataset at different topological scales. In certain implementations, for example, the disclosed systems generate and combine (i) an initial topological construct for a multidimensional dataset at one scale and (ii) a local topological construct for a subset of the multidimensional dataset at another scale to form a varied-scale-topological construct. To identify a region from an initial topological construct to vary in scale, the disclosed systems can determine the relative densities of subsets of multidimensional data corresponding to regions of the initial topological construct and select one or more such regions to change in scale.

公开(公告)号：US20200234110A1

公开(公告)日：2020-07-23

申请号：US16253561

申请日：2019-01-22

Applicant: Adobe Inc.

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

IPC: G06N3/08 ,  H04L29/06 ,  G06N20/00

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.