Automatic Image Correction Using Machine Learning

    公开(公告)号:US20190197670A1

    公开(公告)日:2019-06-27

    申请号:US15855583

    申请日:2017-12-27

    Applicant: Facebook, Inc.

    CPC classification number: G06T5/005 G06K9/00268 G06K9/6256 G06T2207/30201

    Abstract: In one embodiment, a computing system may access a training image and a reference image of a person and an incomplete image. A generate may generate an in-painted image based on the incomplete image, and a discriminator may be used to determine whether each of the in-painted image, the training image, and the reference image is likely generated by the generator. The system may compute losses based on the determinations and update the discriminator accordingly. Using the updated discriminator, the system may determine whether a second in-painted image generated by the generator is likely generated by the generator. The system may compute a loss based on the determination and update the generator accordingly. Once training is complete, the generator may be used to generate a modified version of a given image, such as making the eyes of a person appear open even if they were closed in the input image.

    Automatic image correction using machine learning

    公开(公告)号:US10388002B2

    公开(公告)日:2019-08-20

    申请号:US15855583

    申请日:2017-12-27

    Applicant: Facebook, Inc.

    Abstract: In one embodiment, a computing system may access a training image and a reference image of a person and an incomplete image. A generate may generate an in-painted image based on the incomplete image, and a discriminator may be used to determine whether each of the in-painted image, the training image, and the reference image is likely generated by the generator. The system may compute losses based on the determinations and update the discriminator accordingly. Using the updated discriminator, the system may determine whether a second in-painted image generated by the generator is likely generated by the generator. The system may compute a loss based on the determination and update the generator accordingly. Once training is complete, the generator may be used to generate a modified version of a given image, such as making the eyes of a person appear open even if they were closed in the input image.

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