Sample Selection for Universal Domain Adaptation

Authors

  • Omri Lifshitz Tel Aviv University, Israel
  • Lior Wolf Tel Aviv University, Israel

Keywords:

Transfer/Adaptation/Multi-task/Meta/Automated Learning

Abstract

This paper studies the problem of unsupervised domain adaption in the universal scenario, in which only some of the classes are shared between the source and target domains. We present a scoring scheme that is effective in identifying the samples of the shared classes. The score is used to select samples in the target domain for which to apply specific losses during training; pseudo-labels for high scoring samples and confidence regularization for low scoring samples. Taken together, our method is shown to outperform, by a sizeable margin, the current state of the art on the literature benchmarks.

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Published

2021-05-18

How to Cite

Lifshitz, O., & Wolf, L. (2021). Sample Selection for Universal Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(10), 8592-8600. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17042

Issue

Section

AAAI Technical Track on Machine Learning III