• Corpus ID: 208910752

Less Confusion More Transferable: Minimum Class Confusion for Versatile Domain Adaptation

@article{Jin2019LessCM,
  title={Less Confusion More Transferable: Minimum Class Confusion for Versatile Domain Adaptation},
  author={Ying Jin and Ximei Wang and Mingsheng Long and Jianmin Wang},
  journal={ArXiv},
  year={2019},
  volume={abs/1912.03699}
}
Domain Adaptation (DA) transfers a learning model from a labeled source domain to an unlabeled target domain which follows different distributions. There are a variety of DA scenarios subject to label sets and domain configurations, including closed-set and partial-set DA, as well as multisource and multi-target DA. It is notable that existing DA methods are generally designed only for a specific scenario, and may underperform for scenarios they are not tailored to. Towards a versatile DA… 

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