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    PROTECTING IMAGE FEATURES IN STYLIZED REPRESENTATIONS OF A SOURCE IMAGE
    21.
    发明公开
    PROTECTING IMAGE FEATURES IN STYLIZED REPRESENTATIONS OF A SOURCE IMAGE 审中-公开

    公开(公告)号：US20230215062A1

    公开(公告)日：2023-07-06

    申请号：US17804268

    申请日：2022-05-26

    Applicant: Snap Inc.

    Inventor： Konstantin Gudkov ,  Sergey Demyanov ,  Andrei Zharkov ,  Fedor Zhdanov ,  Vadim Velicodnii

    IPC: G06T11/60 ,  G06N20/20

    CPC classification number: G06T11/60 ,  G06N20/20 ,  G06T2210/44

    Abstract: Systems and methods herein describe an image stylization system. The image stylization system accesses a set of images corresponding to a target domain style, generates a set of paired images using a first machine learning model, analyze the generated set of paired images using a second machine learning model trained to analyze the generated set of paired images based on a plurality of protected feature criteria, determines a set of image transformations for the generated set of pairs, generates a transformed set of paired images by performing the set of image transformations on the set of paired images, and generates stylized images corresponding to the target domain style using a supervised image translation model trained on the transformed set of paired images.

    Domain changes in generative adversarial networks
    22.
    发明授权
    Domain changes in generative adversarial networks 有权

    公开(公告)号：US12148202B2

    公开(公告)日：2024-11-19

    申请号：US17841333

    申请日：2022-06-15

    Applicant: Snap Inc.

    Inventor： Sergey Demyanov ,  Konstantin Gudkov ,  Fedor Zhdanov ,  Andrei Zharkov

    IPC: G06T11/00 ,  G06T3/18 ,  G06T7/68 ,  G06V10/74 ,  G06V10/774 ,  G06V10/82 ,  G06V40/16

    Abstract: An image manipulation system for generating modified images using a generative adversarial network (GAN) trains GANs using domain changes, aligns input images with generated images, classifies and associates target images based on a symmetry, and uses a modified discriminator structure. A method for domain changes includes generating, using a pre-trained GAN trained on a plurality of first target images, a plurality of images, and determining a feature for each of the plurality of images. The method further includes determining the feature for each of a plurality of second target images and matching, based on the feature, second target images of the plurality of second target images with the plurality of images. The method further includes training a discriminator of the pre-trained GAN with the second target images and the plurality of images.

    DEEP FEATURE GENERATIVE ADVERSARIAL NEURAL NETWORKS
    26.
    发明申请
    DEEP FEATURE GENERATIVE ADVERSARIAL NEURAL NETWORKS 有权

    公开(公告)号：US20210383509A1

    公开(公告)日：2021-12-09

    申请号：US17445362

    申请日：2021-08-18

    Applicant: Snap Inc.

    Inventor： Sergey Demyanov ,  Aleksei Podkin ,  Aleksei Stoliar ,  Vadim Velicodnii ,  Fedor Zhdanov

    IPC: G06T5/00 ,  G06T5/10

    Abstract: A mobile device can implement a neural network-based domain transfer scheme to modify an image in a first domain appearance to a second domain appearance. The domain transfer scheme can be configured to detect an object in the image, apply an effect to the image, and blend the image using color space adjustments and blending schemes to generate a realistic result image. The domain transfer scheme can further be configured to efficiently execute on the constrained device by removing operational layers based on resources available on the mobile device.

    Deep feature generative adversarial neural networks
    27.
    发明授权
    Deep feature generative adversarial neural networks 有权

    公开(公告)号：US11120526B1

    公开(公告)日：2021-09-14

    申请号：US16376564

    申请日：2019-04-05

    Applicant: Snap Inc.

    Inventor： Sergey Demyanov ,  Aleksei Podkin ,  Aleksei Stoliar ,  Vadim Velicodnii ,  Fedor Zhdanov

    IPC: G06T5/00 ,  G06T5/10 ,  H04L12/58

    Abstract: A mobile device can implement a neural network-based domain transfer scheme to modify an image in a first domain appearance to a second domain appearance. The domain transfer scheme can be configured to detect an object in the image, apply an effect to the image, and blend the image using color space adjustments and blending schemes to generate a realistic result image. The domain transfer scheme can further be configured to efficiently execute on the constrained device by removing operational layers based on resources available on the mobile device.

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