Deep into The Domain Shift : Transfer Learning through Dependence Regularization

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Scopus Citations
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  • Shumin Ma
  • Zhiri Yuan
  • Dongdong Wang
  • Zhixiang Huang


Original languageEnglish
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Online published6 Jun 2023
Publication statusOnline published - 6 Jun 2023


Classical Domain Adaptation methods acquire transferability by regularizing the overall distributional discrepancies between features in the source domain (labeled) and features in the target domain (unlabeled). They often do not differentiate whether the domain differences come from the marginals or the dependence structures. In many business and financial applications, the labeling function usually has different sensitivities to the changes in the marginals versus changes in the dependence structures. Measuring the overall distributional differences will not be discriminative enough in acquiring transferability. Without the needed structural resolution, the learned transfer is less optimal. This paper proposes a new domain adaptation approach in which one can measure the differences in the internal dependence structure separately from those in the marginals. By optimizing the relative weights among them, the new regularization strategy greatly relaxes the rigidness of the existing approaches. It allows a learning machine to pay special attention to places where the differences matter the most. Experiments on three real-world datasets show that the improvements are quite notable and robust compared to various benchmark domain adaptation models. © 2023 IEEE.

Research Area(s)

  • domain adaptation, regularization, domain divergence, copula