Original paper
Joint distribution matching embedding for unsupervised domain adaptation
Abstract
When the distributions between the source (training) and target (test) datasets are different, the performance of classical statistical learning methods degrades significantly. Domain adaptation (DA) aims at correcting this distribution mismatch and narrowing down the distribution discrepancy. Existing methods mostly focus on correcting the mismatch between the marginal distributions and/or the class-conditional distributions. In this paper, we...
Paper Details
Title
Joint distribution matching embedding for unsupervised domain adaptation
Published Date
Oct 1, 2020
Journal
Volume
412
Pages
115 - 128
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