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    Training neural networks using data augmentation policies
    33.
    发明授权
    Training neural networks using data augmentation policies 有权

    公开(公告)号：US11205099B2

    公开(公告)日：2021-12-21

    申请号：US16833449

    申请日：2020-03-27

    Applicant: Google LLC

    Inventor： Jonathon Shlens ,  Quoc V. Le ,  Ekin Dogus Cubuk ,  Barret Zoph

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

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes obtaining a training data set for training a machine learning model, the training data set comprising a plurality of training inputs; determining a plurality of data augmentation policies, wherein each data augmentation policy defines a procedure for processing a training input to generate a transformed training input; for each data augmentation policy, training the machine learning model using the data augmentation policy; determining, for each data augmentation policy, a quality measure of the machine learning model that has been trained using the data augmentation policy; and selecting a final data augmentation policy based using the quality measures of the machine learning models.

    NEURAL ARCHITECTURE SEARCH USING A PERFORMANCE PREDICTION NEURAL NETWORK
    34.
    发明申请
    NEURAL ARCHITECTURE SEARCH USING A PERFORMANCE PREDICTION NEURAL NETWORK 有权

    公开(公告)号：US20210334624A1

    公开(公告)日：2021-10-28

    申请号：US17365939

    申请日：2021-07-01

    Applicant: Google LLC

    Inventor： Wei Hua ,  Barret Zoph ,  Jonathon Shlens ,  Chenxi Liu ,  Jonathan Huang ,  Jia Li ,  Fei-Fei Li ,  Kevin Patrick Murphy

    IPC: G06N3/04 ,  G06K9/62 ,  G06N3/063 ,  G06N3/08

    Abstract: A method for determining an architecture for a task neural network configured to perform a particular machine learning task is described. The method includes obtaining data specifying a current set of candidate architectures for the task neural network; for each candidate architecture in the current set: processing the data specifying the candidate architecture using a performance prediction neural network having multiple performance prediction parameters, the performance prediction neural network being configured to process the data specifying the candidate architecture in accordance with current values of the performance prediction parameters to generate a performance prediction that characterizes how well a neural network having the candidate architecture would perform after being trained on the particular machine learning task; and generating an updated set of candidate architectures by selecting one or more of the candidate architectures in the current set based on the performance predictions for the candidate architectures in the current set.

    Transforming grayscale images into color images using deep neural networks
    36.
    发明授权
    Transforming grayscale images into color images using deep neural networks 有权

    公开(公告)号：US11087504B2

    公开(公告)日：2021-08-10

    申请号：US16494386

    申请日：2018-05-21

    Applicant: Google LLC

    Inventor： Sergio Guadarrama Cotado ,  Jonathon Shlens ,  David Bieber ,  Mohammad Norouzi ,  Kevin Patrick Murphy ,  Ryan Lienhart Dahl

    IPC: G06T11/00 ,  G06T3/40

    Abstract: Systems and methods for transforming grayscale images into color images using deep neural networks are described. One of the systems include one or more computers and one or more storage devices storing instructions that, when executed by one or more computers, cause the one or more computers to implement a coloring neural network, a refinement neural network, and a subsystem. The coloring neural network is configured to receive a first grayscale image having a first resolution and to process the first grayscale image to generate a first color image having a second resolution lower than the first resolution. The subsystem processes the first color image to generate a set of intermediate image outputs. The refinement neural network is configured to receive the set intermediate image outputs, and to process the set of intermediate image outputs to generate a second color image having a third resolution higher than the second resolution.

    NEURAL ARCHITECTURE SEARCH USING A PERFORMANCE PREDICTION NEURAL NETWORK
    37.
    发明申请
    NEURAL ARCHITECTURE SEARCH USING A PERFORMANCE PREDICTION NEURAL NETWORK 审中-公开

    公开(公告)号：US20200257961A1

    公开(公告)日：2020-08-13

    申请号：US16861491

    申请日：2020-04-29

    Applicant: Google LLC

    Inventor： Wei Hua ,  Barret Zoph ,  Jonathon Shlens ,  Chenxi Liu ,  Jonathan Huang ,  Jia Li ,  Fei-Fei Li ,  Kevin Patrick Murphy

    IPC: G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06K9/62

    Abstract: A method for determining an architecture for a task neural network configured to perform a particular machine learning task is described. The method includes obtaining data specifying a current set of candidate architectures for the task neural network; for each candidate architecture in the current set: processing the data specifying the candidate architecture using a performance prediction neural network having multiple performance prediction parameters, the performance prediction neural network being configured to process the data specifying the candidate architecture in accordance with current values of the performance prediction parameters to generate a performance prediction that characterizes how well a neural network having the candidate architecture would perform after being trained on the particular machine learning task; and generating an updated set of candidate architectures by selecting one or more of the candidate architectures in the current set based on the performance predictions for the candidate architectures in the current set.

    NEURAL ARCHITECTURE SEARCH FOR DENSE IMAGE PREDICTION TASKS
    38.
    发明申请
    NEURAL ARCHITECTURE SEARCH FOR DENSE IMAGE PREDICTION TASKS 审中-公开

    公开(公告)号：US20190370648A1

    公开(公告)日：2019-12-05

    申请号：US16425900

    申请日：2019-05-29

    Applicant: Google LLC

    Inventor： Barret Zoph ,  Jonathon Shlens ,  Yukun Zhu ,  Maxwell Donald Emmet Collins ,  Liang-Chieh Chen ,  Gerhard Florian Schroff ,  Hartwig Adam ,  Georgios Papandreou

    IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06N20/00 ,  G06F17/15

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes obtaining training data for a dense image prediction task; and determining an architecture for a neural network configured to perform the dense image prediction task, comprising: searching a space of candidate architectures to identify one or more best performing architectures using the training data, wherein each candidate architecture in the space of candidate architectures comprises (i) the same first neural network backbone that is configured to receive an input image and to process the input image to generate a plurality of feature maps and (ii) a different dense prediction cell configured to process the plurality of feature maps and to generate an output for the dense image prediction task; and determining the architecture for the neural network based on the best performing candidate architectures.

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