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    Optimizer based prunner for neural networks
    12.
    发明授权
    Optimizer based prunner for neural networks 有权

    公开(公告)号：US11580400B1

    公开(公告)日：2023-02-14

    申请号：US16586635

    申请日：2019-09-27

    Applicant: Snap Inc.

    Inventor： Enxu Yan ,  Sergey Tulyakov ,  Aleksei Podkin ,  Aleksei Stoliar

    IPC: G06N3/08 ,  G06N3/082 ,  G06N3/04 ,  G06F17/16 ,  G06T7/10

    Abstract: A neural network pruning system can sparsely prune neural network models using an optimizer based approach that is agnostic to the model architecture being pruned. The neural network pruning system can prune by operating on the parameter vector of the full model and the gradient vector of the loss function with respect to the model parameters. The neural network pruning system can iteratively update parameters based on the gradients, while zeroing out as many parameters as possible based a preconfigured penalty.

    DEEP FEATURE GENERATIVE ADVERSARIAL NEURAL NETWORKS
    13.
    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
    15.
    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.

    IMAGE LANDMARK DETECTION
    16.
    发明申请
    IMAGE LANDMARK DETECTION 有权

    公开(公告)号：US20210192198A1

    公开(公告)日：2021-06-24

    申请号：US17138177

    申请日：2020-12-30

    Applicant: Snap Inc.

    Inventor： Sergey Tulyakov ,  Roman Furko ,  Aleksei Stoliar

    IPC: G06K9/00 ,  G06K9/62 ,  G06T7/246 ,  G06T7/73 ,  G06T11/60

    Abstract: A landmark detection system can more accurately detect landmarks in images using a detection scheme that penalizes for dispersion parameters, such as variance or scale. The landmark detection system can be trained using both labeled and unlabeled training data in a semi-supervised approach. The landmark detection system can further implement tracking of an object across multiple images using landmark data.

    STEP DISTILLATION FOR LATENT DIFFUSION MODELS
    17.
    发明申请
    STEP DISTILLATION FOR LATENT DIFFUSION MODELS 有权

    公开(公告)号：US20240394843A1

    公开(公告)日：2024-11-28

    申请号：US18434411

    申请日：2024-02-06

    Applicant: Snap Inc.

    Inventor： Pavlo Chemerys ,  Colin Eles ,  Ju Hu ,  Qing Jin ,  Yanyu Li ,  Ergeta Muca ,  Jian Ren ,  Dhritiman Sagar ,  Aleksei Stoliar ,  Sergey Tulyakov ,  Huan Wang

    IPC: G06T5/60 ,  G06T5/70

    Abstract: Described is a system for improving machine learning models by accessing a first latent diffusion machine learning model, the first latent diffusion machine learning model trained to perform a first number of denoising steps, accessing a second latent diffusion machine learning model that was derived from the first latent diffusion machine learning model, the second latent diffusion machine learning model trained to perform a second number of denoising steps, generating noise data, processing the noise data via the first latent diffusion machine learning model to generate one or more first images, processing the noise data via the second latent diffusion machine learning model to generate one or more second images, and modify a parameter of the second latent diffusion machine learning model based on a comparison of the one or more first images with the one or more second images.

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