A Sample Selection Approach for Universal Domain Adaptation

• URL: http://arxiv.org/abs/2001.05071v1
• Date: Tue, 14 Jan 2020 22:28:43 GMT
• Title: A Sample Selection Approach for Universal Domain Adaptation
• Authors: Omri Lifshitz and Lior Wolf
• Abstract summary: We study the problem of unsupervised domain adaption in the universal scenario. 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.
• Score: 94.80212602202518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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