A Sample Selection Approach for Universal Domain Adaptation

Omri Lifshitz; Lior Wolf

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arXiv · 2001.05071

A Sample Selection Approach for Universal Domain Adaptation

Omri Lifshitz·Lior Wolf

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Abstract

We study 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 which samples in the target domain to pseudo-label during training. Another loss term encourages diversity of labels within each batch. Taken together, our method is shown to outperform, by a sizable margin, the current state of the art on the literature benchmarks.

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Omri Lifshitz, Lior Wolf. 2020-01-14. A Sample Selection Approach for Universal Domain Adaptation. https://arxiv.org/abs/2001.05071

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