Domain adaptation using probability distribution distance

    公开(公告)号:US11188795B1

    公开(公告)日:2021-11-30

    申请号:US16665354

    申请日:2019-10-28

    Applicant: Apple Inc.

    Abstract: Methods and systems that train a neural network to classify inputs using a first set of labeled inputs corresponding to a source domain and adapt that neural network to classify inputs from another domain. The neural network includes a generator network and two or more classifier networks. The generator network is trained to receive inputs and generate features. The two or more classifier networks are trained to classify those features into classes to obtain class probability predictions. The neural network is adapted to a target domain, for example, by training the classifier networks to maximize a Wasserstein distance-based discrepancy between the class probability predictions of the classifier networks, by training the classifier networks to increase Wasserstein distance-based discrepancy or by training the generator network to minimize the Wasserstein distance-based discrepancy between the class probability predictions of the multiple classifier networks, or both.

    End-to-end room layout estimation

    公开(公告)号:US11188787B1

    公开(公告)日:2021-11-30

    申请号:US16582722

    申请日:2019-09-25

    Applicant: Apple Inc.

    Abstract: Systems, methods, and computer readable media to implementing an end-to-end room layout estimation are described. A room layout estimation engine performs feature extraction on an image frame to generate a first set of coefficients for a first room layout class and a second set of coefficients for a second room layout class. Afterwards, the room layout estimation engine generates a first set of planes according to the first set of coefficients and a second set of planes according to the second set of coefficients. The room layout estimation engine generates a first prediction plane according to the first set of planes and a second prediction plane according to the second set of planes. Afterwards, the room layout estimation engine merges the first prediction plane and the second prediction plane to generate a predicted room layout for the room.

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