• Corpus ID: 237267340

ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation

  title={ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation},
  author={Kai Li and Chang Liu and Handong Zhao and Yulun Zhang and Yun Raymond Fu},
  • Kai Li, Chang Liu, +2 authors Y. Fu
  • Published 19 April 2021
  • Computer Science
This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled samples and a few labeled samples in the target domain, with the help of labeled samples from a source domain. Several SSDA methods have been proposed recently, which however fail to fully exploit the value of the few labeled target samples. In this paper, we propose Enhanced Categorical Alignment and Consistency Learning… 

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