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Bures Joint Distribution Alignment with Dynamic Margin for Unsupervised Domain Adaptation
[article]
2022
arXiv
pre-print
Unsupervised domain adaptation (UDA) is one of the prominent tasks of transfer learning, and it provides an effective approach to mitigate the distribution shift between the labeled source domain and the unlabeled target domain. Prior works mainly focus on aligning the marginal distributions or the estimated class-conditional distributions. However, the joint dependency among the feature and the label is crucial for the adaptation task and is not fully exploited. To address this problem, we
arXiv:2203.06836v1
fatcat:55z2fizqsveexcc6ids5ze7iem