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    Providing insights and suggestions for journeys
    41.
    发明授权
    Providing insights and suggestions for journeys 有权

    公开(公告)号：US11861636B2

    公开(公告)日：2024-01-02

    申请号：US16910357

    申请日：2020-06-24

    Applicant: ADOBE INC.

    Inventor： Pankhri Singhai ,  Piyush Gupta ,  Balaji Krishnamurthy ,  Jayakumar Subramanian ,  Nikaash Puri

    IPC: G06Q30/02 ,  G06Q30/0204 ,  G06N20/00 ,  G06Q30/0201 ,  G06Q10/0633

    CPC classification number: G06Q30/0205 ,  G06N20/00 ,  G06Q10/0633 ,  G06Q30/0201

    Abstract: Methods and systems are provided for generating and providing insights associated with a journey. In embodiments described herein, journey data associated with a journey is obtained. A journey can include journey paths indicating workflows through which audience members can traverse. The journey data can include audience member attributes (e.g., demographics) and labels indicating journey paths traversed by audience members. A set of audience segments are determined that describe a set of audience members traversing a particular journey path. The set of audience segments can be determined using the journey data to train a segmentation model and, thereafter, analyzing the segmentation model to identify patterns that indicate audience segments associated with the particular journey path. An indication of the set of audience segments that describe the set of audience members traversing the particular journey path can be provided for display.

    Systems and methods of training neural networks against adversarial attacks
    42.
    发明授权
    Systems and methods of training neural networks against adversarial attacks 有权

    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

    IPC: G06N3/08 ,  G06F21/57 ,  G06N5/046 ,  G06F18/24 ,  G06F18/214 ,  G06N3/048

    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.

    Attributionally robust training for weakly supervised localization and segmentation
    45.
    发明授权
    Attributionally robust training for weakly supervised localization and segmentation 有权

    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

    IPC: G06K9/00 ,  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.

    Similarity propagation for one-shot and few-shot image segmentation
    47.
    发明授权
    Similarity propagation for one-shot and few-shot image segmentation 有权

    公开(公告)号：US11367271B2

    公开(公告)日：2022-06-21

    申请号：US16906954

    申请日：2020-06-19

    Applicant: ADOBE INC.

    Inventor： Mayur Hemani ,  Siddhartha Gairola ,  Ayush Chopra ,  Balaji Krishnamurthy ,  Jonas Dahl

    IPC: G06K9/62 ,  G06V10/46 ,  G06K9/46

    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for one-shot and few-shot image segmentation on classes of objects that were not represented during training. In some embodiments, a dual prediction scheme may be applied in which query and support masks are jointly predicted using a shared decoder, which aids in similarity propagation between the query and support features. Additionally or alternatively, foreground and background attentive fusion may be applied to utilize cues from foreground and background feature similarities between the query and support images. Finally, to prevent overfitting on class-conditional similarities across training classes, input channel averaging may be applied for the query image during training. Accordingly, the techniques described herein may be used to achieve state-of-the-art performance for both one-shot and few-shot segmentation tasks.

    Rule determination for black-box machine-learning models
    48.
    发明授权
    Rule determination for black-box machine-learning models 有权

    公开(公告)号：US11354590B2

    公开(公告)日：2022-06-07

    申请号：US15812991

    申请日：2017-11-14

    Applicant: Adobe Inc.

    Inventor： Piyush Gupta ,  Sukriti Verma ,  Pratiksha Agarwal ,  Nikaash Puri ,  Balaji Krishnamurthy

    IPC: G06N20/00 ,  G06N5/04 ,  G06N3/12 ,  G06N3/08 ,  G06N5/00 ,  G06N20/20 ,  G06N5/02

    Abstract: Rule determination for black-box machine-learning models (BBMLMs) is described. These rules are determined by an interpretation system to describe operation of a BBMLM to associate inputs to the BBMLM with observed outputs of the BBMLM and without knowledge of the logic used in operation by the BBMLM to make these associations. To determine these rules, the interpretation system initially generates a proxy black-box model to imitate the behavior of the BBMLM based solely on data indicative of the inputs and observed outputs—since the logic actually used is not available to the system. The interpretation system generates rules describing the operation of the BBMLM by combining conditions—identified based on output of the proxy black-box model—using a genetic algorithm. These rules are output as if-then statements configured with an if-portion formed as a list of the conditions and a then-portion having an indication of the associated observed output.

    Behavioral prediction for targeted end users
    50.
    发明授权
    Behavioral prediction for targeted end users 有权

    公开(公告)号：US11301761B2

    公开(公告)日：2022-04-12

    申请号：US16241456

    申请日：2019-01-07

    Applicant: Adobe Inc.

    Inventor： Balaji Krishnamurthy ,  Tushar Singla

    IPC: G06N5/02 ,  G06Q30/02 ,  G06F16/901 ,  G06F16/9535 ,  G06Q30/00 ,  G06N20/00

    Abstract: Behavioral prediction for targeted end users is described. In one or more example embodiments, a computer-readable storage medium has multiple instructions that cause one or more processors to perform multiple operations. Targeted selectstream data is obtained from one or more indications of data object requests corresponding to a targeted end user. A targeted directed graph is constructed based on the targeted selectstream data. A targeted graph feature vector is computed based on one or more invariant features associated with the targeted directed graph. A behavioral prediction is produced for the targeted end user by applying a prediction model to the targeted graph feature vector. In one or more example embodiments, the prediction model is generated based on multiple graph feature vectors respectively corresponding to multiple end users. In one or more example embodiments, a tailored opportunity is determined responsive to the behavioral prediction and issued to the targeted end user.

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