Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice

20 Feb 2020Yabin ZhangBin DengHui TangLei ZhangKui Jia

In this paper, we study the formalism of unsupervised multi-class domain adaptation (multi-class UDA), which underlies some recent algorithms whose learning objectives are only motivated empirically. A Multi-Class Scoring Disagreement (MCSD) divergence is presented by aggregating the absolute margin violations in multi-class classification; the proposed MCSD is able to fully characterize the relations between any pair of multi-class scoring hypotheses... (read more)

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