Model disentanglement for domain adaptation

    公开(公告)号:US12019726B2

    公开(公告)日:2024-06-25

    申请号:US17655506

    申请日:2022-03-18

    CPC分类号: G06F21/32 G06N5/022

    摘要: Certain aspects of the present disclosure provide techniques for improved domain adaptation in machine learning. A feature tensor is generated by processing input data using a feature extractor. A first set of logits is generated by processing the feature tensor using a domain-agnostic classifier, and a second set of logits is generated by processing the feature tensor using a domain-specific classifier. A loss is computed based at least in part on the first set of logits and the second set of logits, where the loss includes a divergence loss component. The feature extractor, the domain-agnostic classifier, and the domain-specific classifier are refined using the loss.