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Self-corrected unsupervised domain adaptation

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Abstract

Unsupervised domain adaptation (UDA), which aims to use knowledge from a label-rich source domain to help learn unlabeled target domain, has recently attracted much attention. UDA methods mainly concentrate on source classification and distribution alignment between domains to expect the correct target prediction. While in this paper, we attempt to learn the target prediction end to end directly, and develop a Self-corrected unsupervised domain adaptation (SCUDA) method with probabilistic label correction. SCUDA adopts a probabilistic label corrector to learn and correct the target labels directly. Specifically, besides model parameters, those target pseudo-labels are also updated in learning and corrected by the anchor-variable, which preserves the class candidates for samples. Experiments on real datasets show the competitiveness of SCUDA.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61876091, 61772284), the China Postdoctoral Science Foundation (2019M651918), and the Open Foundation of MIIT Key Laboratory of Pattern Analysis and Machine Intelligence.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Posts & Telecommunications, Nanjing, 210046, China

    Yunyun Wang & Chao Wang

  2. School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China

    Hui Xue

  3. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210023, China

    Songcan Chen

Authors
  1. Yunyun Wang
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  2. Chao Wang
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  3. Hui Xue
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  4. Songcan Chen
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Corresponding author

Correspondence to Yunyun Wang.

Additional information

Yunyun Wang received the PhD degree in Computer Science and Technology from Nanjing University of Aeronautics and Astronautics, China in 2012. She is currently with the School of Computer Science and Technology in Nanjing University of Posts and Telecommunications, China. Her current research interests include pattern recognition, machine learning, and neural computing.

Chao Wang received a BS degree from Nanjing University of Technology, China in 2018. He is a Master’s student in Computer Science and Technology at Nanjing University of Posts and Telecommunications, China. His current research interests include transfer learning, machine learning, and neural computing.

Hui Xue received her MS degree in mathematics from Nanjing University of Aeronautics & Astronautics (NUAA), China in 2005. And she also received her PhD degree in computer application technology at NUAA, China in 2008. Since 2009, she has been with the School of Computer Science & Engineering at Southeast University, China. Her research interests include pattern recognition, machine learning, and neural computing.

Songcan Chen received his BS degree in mathematics from Hangzhou University (now merged into Zhejiang University), China in 1983. In 1985, he completed his MS degree in computer applications at Shanghai Jiaotong University and then worked at NUAA in January 1986. There he received a PhD degree in communication and information systems in 1997. Since 1998, as a full-time professor, he has been with the College of Computer Science & Technology at NUAA, China. His research interests include pattern recognition, machine learning and neural computing. He is also an IAPR Fellow.

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Wang, Y., Wang, C., Xue, H. et al. Self-corrected unsupervised domain adaptation. Front. Comput. Sci. 16, 165323 (2022). https://doi.org/10.1007/s11704-021-1010-8

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