CONTROLLED STYLE-CONTENT IMAGE GENERATION BASED ON DISENTANGLING CONTENT AND STYLE

    公开(公告)号:US20210264236A1

    公开(公告)日:2021-08-26

    申请号:US16802440

    申请日:2020-02-26

    Applicant: ADOBE INC.

    Abstract: Embodiments of the present disclosure are directed towards improved models trained using unsupervised domain adaptation. In particular, a style-content adaptation system provides improved translation during unsupervised domain adaptation by controlling the alignment of conditional distributions of a model during training such that content (e.g., a class) from a target domain is correctly mapped to content (e.g., the same class) in a source domain. The style-content adaptation system improves unsupervised domain adaptation using independent control over content (e.g., related to a class) as well as style (e.g., related to a domain) to control alignment when translating between the source and target domain. This independent control over content and style can also allow for images to be generated using the style-content adaptation system that contain desired content and/or style.

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