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    SUPERVISED LEARNING TECHNIQUES FOR ENCODER TRAINING
    26.
    发明申请
    SUPERVISED LEARNING TECHNIQUES FOR ENCODER TRAINING 有权

    公开(公告)号：US20220121932A1

    公开(公告)日：2022-04-21

    申请号：US17384378

    申请日：2021-07-23

    Applicant: Adobe Inc.

    Inventor： Ratheesh Kalarot ,  Wei-An Lin ,  Cameron Smith ,  Zhixin Shu ,  Baldo Faieta ,  Shabnam Ghadar ,  Jingwan Lu ,  Aliakbar Darabi ,  Jun-Yan Zhu ,  Niloy Mitra ,  Richard Zhang ,  Elya Shechtman

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Systems and methods train an encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The encoder is trained by providing an input training image to the encoder and producing, by the encoder, a latent space representation of the input training image. The latent space representation is provided as input to the GAN to generate a generated training image. A latent code is sampled from a latent space associated with the GAN and the sampled latent code is provided as input to the GAN. The GAN generates a synthetic training image based on the sampled latent code. The sampled latent code is provided as input to the encoder to produce a synthetic training code. The encoder is updated by minimizing a loss between the generated training image and the input training image, and the synthetic training code and the sampled latent code.

    NON-LINEAR LATENT FILTER TECHNIQUES FOR IMAGE EDITING
    27.
    发明申请
    NON-LINEAR LATENT FILTER TECHNIQUES FOR IMAGE EDITING 有权

    公开(公告)号：US20220121876A1

    公开(公告)日：2022-04-21

    申请号：US17468498

    申请日：2021-09-07

    Applicant: Adobe Inc.

    Inventor： Ratheesh Kalarot ,  Wei-An Lin ,  Baldo Faieta ,  Shabnam Ghadar

    IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06N20/20

    Abstract: Systems and methods use a non-linear latent filter neural network for editing an image. An image editing system trains a first neural network by minimizing a loss based upon a predicted attribute value for a target attribute in a training image. The image editing system obtains a latent space representation of an input image to be edited and a target attribute value for the target attribute in the input image. The image editing system provides the latent space representation and the target attribute value as input to the trained first neural network for modifying the target attribute in the input image to generate a modified latent space representation of the input image. The image editing system provides the modified latent space representation as input to a second neural network to generate an output image with a modification to the target attribute corresponding to the target attribute value.

    IMAGE RELIGHTING
    28.
    发明申请
    IMAGE RELIGHTING 有权

    公开(公告)号：US20250069299A1

    公开(公告)日：2025-02-27

    申请号：US18452827

    申请日：2023-08-21

    Applicant: ADOBE INC.

    Inventor： Kevin Duarte ,  Wei-An Lin ,  Ratheesh Kalarot ,  Shabnam Ghadar ,  Jingwan Lu ,  Elya Shechtman

    IPC: G06T11/60

    Abstract: One or more aspects of a method, apparatus, and non-transitory computer readable medium include obtaining an input latent vector for an image generation network and a target lighting representation. A modified latent vector is generated based on the input latent vector and the target lighting representation, and an image generation network generates an image based on the modified latent vector using.

    ANONYMIZING DIGITAL IMAGES UTILIZING A GENERATIVE ADVERSARIAL NEURAL NETWORK
    30.
    发明公开
    ANONYMIZING DIGITAL IMAGES UTILIZING A GENERATIVE ADVERSARIAL NEURAL NETWORK 审中-公开

    公开(公告)号：US20240143835A1

    公开(公告)日：2024-05-02

    申请号：US18052121

    申请日：2022-11-02

    Applicant: Adobe Inc.

    Inventor： Siavash Khodadadeh ,  Ratheesh Kalarot ,  Shabnam Ghadar ,  Yannick Hold-Geoffroy

    IPC: G06F21/62 ,  G06N3/0455 ,  G06N3/0475

    CPC classification number: G06F21/6254 ,  G06N3/0455 ,  G06N3/0475

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating anonymized digital images utilizing a face anonymization neural network. In some embodiments, the disclosed systems utilize a face anonymization neural network to extract or encode a face anonymization guide that encodes face attribute features, such as gender, ethnicity, age, and expression. In some cases, the disclosed systems utilize the face anonymization guide to inform the face anonymization neural network in generating synthetic face pixels for anonymizing a digital image while retaining attributes, such as gender, ethnicity, age, and expression. The disclosed systems learn parameters for a face anonymization neural network for preserving face attributes, accounting for multiple faces in digital images, and generating synthetic face pixels for faces in profile poses.

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