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    Generative adversarial networks with temporal and spatial discriminators for efficient video generation
    1.
    发明授权
    Generative adversarial networks with temporal and spatial discriminators for efficient video generation 有权

    公开(公告)号：US12277672B2

    公开(公告)日：2025-04-15

    申请号：US17613694

    申请日：2020-05-22

    Applicant: DeepMind Technologies Limited

    Inventor： Aidan Clark ,  Jeffrey Donahue ,  Karen Simonyan

    IPC: G06T3/4046 ,  G06N3/045 ,  G06N3/08

    Abstract: The present disclosure proposes the use of a dual discriminator network that comprises a temporal discriminator network for discriminating based on temporal features of a series of images and a spatial discriminator network for discriminating based on spatial features of individual images. The training methods described herein provide improvements in computational efficiency. This is achieved by applying the spatial discriminator network to a set of one or more images that have reduced temporal resolution and applying the temporal discriminator network to a set of images that have reduced spatial resolution. This allows each of the discriminator networks to be applied more efficiently in order to produce a discriminator score for use in training the generator, whilst maintaining accuracy of the discriminator network. In addition, this allows a generator network to be trained to more accurately generate sequences of images, through the use of the improved discriminator.

    HIGH FIDELITY SPEECH SYNTHESIS WITH ADVERSARIAL NETWORKS
    2.
    发明申请
    HIGH FIDELITY SPEECH SYNTHESIS WITH ADVERSARIAL NETWORKS 有权

    公开(公告)号：US20210089909A1

    公开(公告)日：2021-03-25

    申请号：US17032578

    申请日：2020-09-25

    Applicant: DeepMind Technologies Limited

    Inventor： Mikolaj Binkowski ,  Karen Simonyan ,  Jeffrey Donahue ,  Aidan Clark ,  Sander Etienne Lea Dieleman ,  Erich Konrad Elsen ,  Luis Carlos Cobo Rus ,  Norman Casagrande

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

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output audio examples using a generative neural network. One of the methods includes obtaining a training conditioning text input; processing a training generative input comprising the training conditioning text input using a feedforward generative neural network to generate a training audio output; processing the training audio output using each of a plurality of discriminators, wherein the plurality of discriminators comprises one or more conditional discriminators and one or more unconditional discriminators; determining a first combined prediction by combining the respective predictions of the plurality of discriminators; and determining an update to current values of a plurality of generative parameters of the feedforward generative neural network to increase a first error in the first combined prediction.

    LARGE SCALE GENERATIVE NEURAL NETWORK MODEL WITH INFERENCE FOR REPRESENTATION LEARNING USING ADVERSIAL TRAINING
    5.
    发明申请
    LARGE SCALE GENERATIVE NEURAL NETWORK MODEL WITH INFERENCE FOR REPRESENTATION LEARNING USING ADVERSIAL TRAINING 审中-公开

    公开(公告)号：US20200372370A1

    公开(公告)日：2020-11-26

    申请号：US16882352

    申请日：2020-05-22

    Applicant: DeepMind Technologies Limited

    Inventor： Jeffrey Donahue ,  Karen Simonyan

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a generator neural network and an encoder neural network. The generator neural network generates, based on a set of latent values, data items which are samples of a distribution. The encoder neural network generates a set of latent values for a respective data item. The training method comprises jointly training the generator neural network, the encoder neural network and a discriminator neural network configured to distinguish between samples generated by the generator network and samples of the distribution which are not generated by the generator network. The discriminator neural network is configured to distinguish by processing, by the discriminator neural network, an input pair comprising a sample part and a latent part.

    Large scale generative neural network model with inference for representation learning using adversarial training
    6.
    发明授权
    Large scale generative neural network model with inference for representation learning using adversarial training 有权

    公开(公告)号：US11875269B2

    公开(公告)日：2024-01-16

    申请号：US16882352

    申请日：2020-05-22

    Applicant: DeepMind Technologies Limited

    Inventor： Jeffrey Donahue ,  Karen Simonyan

    IPC: G06N3/088 ,  G06N3/045

    CPC classification number: G06N3/088 ,  G06N3/045

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a generator neural network and an encoder neural network. The generator neural network generates, based on a set of latent values, data items which are samples of a distribution. The encoder neural network generates a set of latent values for a respective data item. The training method comprises jointly training the generator neural network, the encoder neural network and a discriminator neural network configured to distinguish between samples generated by the generator network and samples of the distribution which are not generated by the generator network. The discriminator neural network is configured to distinguish by processing, by the discriminator neural network, an input pair comprising a sample part and a latent part.

    Generative Adversarial Networks with Temporal and Spatial Discriminators for Efficient Video Generation
    8.
    发明申请
    Generative Adversarial Networks with Temporal and Spatial Discriminators for Efficient Video Generation 有权

    公开(公告)号：US20220230276A1

    公开(公告)日：2022-07-21

    申请号：US17613694

    申请日：2020-05-22

    Applicant: DeepMind Technologies Limited

    Inventor： Aidan Clark ,  Jeffrey Donahue ,  Karen Simonyan

    IPC: G06T3/40 ,  G06N3/08 ,  G06N3/04

    Abstract: The present disclosure proposes the use of a dual discriminator network that comprises a temporal discriminator network for discriminating based on temporal features of a series of images and a spatial discriminator network for discriminating based on spatial features of individual images. The training methods described herein provide improvements in computational efficiency. This is achieved by applying the spatial discriminator network to a set of one or more images that have reduced temporal resolution and applying the temporal discriminator network to a set of images that have reduced spatial resolution. This allows each of the discriminator networks to be applied more efficiently in order to produce a discriminator score for use in training the generator, whilst maintaining accuracy of the discriminator network. In addition, this allows a generator network to be trained to more accurately generate sequences of images, through the use of the improved discriminator.

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