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    GENERATING REALISTIC SYNTHETIC DATA WITH ADVERSARIAL NETS
    2.
    发明申请
    GENERATING REALISTIC SYNTHETIC DATA WITH ADVERSARIAL NETS 有权

    公开(公告)号：US20230081346A1

    公开(公告)日：2023-03-16

    申请号：US18046871

    申请日：2022-10-14

    Applicant: Apple Inc.

    Inventor： Ashish Shrivastava ,  Tomas J. Pfister ,  Cuneyt O. Tuzel ,  Russell Y. Webb ,  Joshua Matthew Susskind

    IPC: G06N3/04 ,  G06N3/08

    Abstract: A generative network may be learned in an adversarial setting with a goal of modifying synthetic data such that a discriminative network may not be able to reliably tell the difference between refined synthetic data and real data. The generative network and discriminative network may work together to learn how to produce more realistic synthetic data with reduced computational cost. The generative network may iteratively learn a function that synthetic data with a goal of generating refined synthetic data that is more difficult for the discriminative network to differentiate from real data, while the discriminative network may be configured to iteratively learn a function that classifies data as either synthetic or real. Over multiple iterations, the generative network may learn to refine the synthetic data to produce refined synthetic data on which other machine learning models may be trained.

    Generating more realistic synthetic data with adversarial nets
    4.
    发明授权
    Generating more realistic synthetic data with adversarial nets 有权

    公开(公告)号：US11475276B1

    公开(公告)日：2022-10-18

    申请号：US15804900

    申请日：2017-11-06

    Applicant: Apple Inc.

    Inventor： Ashish Shrivastava ,  Tomas J. Pfister ,  Cuneyt O. Tuzel ,  Russell Y. Webb ,  Joshua Matthew Susskind

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

    Abstract: A generative network may be learned in an adversarial setting with a goal of modifying synthetic data such that a discriminative network may not be able to reliably tell the difference between refined synthetic data and real data. The generative network and discriminative network may work together to learn how to produce more realistic synthetic data with reduced computational cost. The generative network may iteratively learn a function that synthetic data with a goal of generating refined synthetic data that is more difficult for the discriminative network to differentiate from real data, while the discriminative network may be configured to iteratively learn a function that classifies data as either synthetic or real. Over multiple iterations, the generative network may learn to refine the synthetic data to produce refined synthetic data on which other machine learning models may be trained.

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