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公开(公告)号：US11468314B1
公开(公告)日：2022-10-11
申请号：US16129553
申请日：2018-09-12
Applicant: ADOBE INC.
Inventor： Mayank Singh , Abhishek Sinha , Balaji Krishnamurthy
Abstract: Embodiments disclosed herein describe systems, methods, and products that generate trained neural networks that are robust against adversarial attacks. During a training phase, an illustrative computer may iteratively optimize a loss function that may include a penalty for ill-conditioned weight matrices in addition to a penalty for classification errors. Therefore, after the training phase, the trained neural network may include one or more well-conditioned weight matrices. The one or more well-conditioned weight matrices may minimize the effect of perturbations within an adversarial input thereby increasing the accuracy of classification of the adversarial input. By contrast, conventional training approaches may merely reduce the classification errors using backpropagation, and, as a result, any perturbation in an input is prone to generate a large effect on the output.
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公开(公告)号：US11109084B2
公开(公告)日：2021-08-31
申请号：US16694612
申请日：2019-11-25
Applicant: Adobe Inc.
Inventor： Pankhri Singhai , Sundeep Parsa , Piyush Gupta , Nikaash Puri , Eshita Shah , Balaji Krishnamurthy , Nupur Kumari , Mayank Singh , Akash Rupela
IPC: H04N21/25 , H04N21/2668 , H04N21/258 , H04N21/475 , G06N20/00 , H04N21/81 , G06Q30/02
Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.
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公开(公告)号：US11734565B2
公开(公告)日：2023-08-22
申请号：US17805405
申请日：2022-06-03
Applicant: ADOBE INC.
Inventor： Mayank Singh , Abhishek Sinha , Balaji Krishnamurthy
CPC classification number: G06N3/08 , G06F18/214 , G06F18/24 , G06F21/577 , G06N3/048 , G06N5/046
Abstract: Embodiments disclosed herein describe systems, methods, and products that generate trained neural networks that are robust against adversarial attacks. During a training phase, an illustrative computer may iteratively optimize a loss function that may include a penalty for ill-conditioned weight matrices in addition to a penalty for classification errors. Therefore, after the training phase, the trained neural network may include one or more well-conditioned weight matrices. The one or more well-conditioned weight matrices may minimize the effect of perturbations within an adversarial input thereby increasing the accuracy of classification of the adversarial input. By contrast, conventional training approaches may merely reduce the classification errors using backpropagation, and, as a result, any perturbation in an input is prone to generate a large effect on the output.
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公开(公告)号：US11631156B2
公开(公告)日：2023-04-18
申请号：US17088120
申请日：2020-11-03
Applicant: ADOBE INC.
Inventor： Mayank Singh , Parth Patel , Nupur Kumari , Balaji Krishnamurthy
Abstract: This disclosure includes technologies for image processing, particularly for image generation and editing in a configurable semantic direction. A generative adversarial network is trained with an auxiliary network with an auxiliary task that is designed to disentangle the latent space of the generative adversarial network. Resultantly, a new type of GAN is created to improve image generation or editing in both conditional and unconditional settings.
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Patent Agency Ranking
公开(公告)号：US11544495B2
公开(公告)日：2023-01-03
申请号：US16926511
申请日：2020-07-10
Applicant: Adobe Inc.
Inventor： Mayank Singh , Balaji Krishnamurthy , Nupur Kumari , Puneet Mangla
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.
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公开(公告)号：US11308353B2
公开(公告)日：2022-04-19
申请号：US16661617
申请日：2019-10-23
Applicant: Adobe Inc.
Inventor： Mayank Singh , Puneet Mangla , Nupur Kumari , Balaji Krishnamurthy , Abhishek Sinha
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for training a classification neural network to classify digital images in few-shot tasks based on self-supervision and manifold mixup. For example, the disclosed systems can train a feature extractor as part of a base neural network utilizing self-supervision and manifold mixup. Indeed, the disclosed systems can apply manifold mixup regularization over a feature manifold learned via self-supervised training such as rotation training or exemplar training. Based on training the feature extractor, the disclosed systems can also train a classifier to classify digital images into novel classes not present within the base classes used to train the feature extractor.
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公开(公告)号：US20210319473A1
公开(公告)日：2021-10-14
申请号：US17355907
申请日：2021-06-23
Applicant: Adobe Inc.
Inventor： Pankhri Singhai , Sundeep Parsa , Piyush Gupta , Nupur Kumari , Nikaash Puri , Mayank Singh , Eshita Shah , Balaji Krishnamurthy , Akash Rupela
Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.
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公开(公告)号：US20200053403A1
公开(公告)日：2020-02-13
申请号：US16057729
申请日：2018-08-07
Applicant: Adobe Inc.
Inventor： Pankhri Singhai , Sundeep Parsa , Piyush Gupta , Nikaash Puri , Eshita Shah , Balaji Krishnamurthy , Nupur Kumari , Mayank Singh , Akash Rupela
IPC: H04N21/25 , G06N99/00 , H04N21/475 , H04N21/258 , H04N21/2668
Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.
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公开(公告)号：US20200051118A1
公开(公告)日：2020-02-13
申请号：US16057743
申请日：2018-08-07
Applicant: Adobe Inc.
Inventor： Pankhri Singhai , Sundeep Parsa , Piyush Gupta , Nupur Kumari , Nikaash Puri , Mayank Singh , Eshita Shah , Balaji Krishnamurthy , Akash Rupela
Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.
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