Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation

30 Jan 2019Jongyeong LeeNontawat CharoenphakdeeSeiichi KurokiMasashi Sugiyama

Appropriately evaluating the discrepancy between domains is essential for the success of unsupervised domain adaptation. In this paper, we first point out that existing discrepancy measures are less informative when complex models such as deep neural networks are used, in addition to the facts that they can be computationally highly demanding and their range of applications is limited only to binary classification... (read more)

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