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Domain compensatory adversarial networks for partial domain adaptation

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Abstract

Recently, domain adaptation has stimulated interest in the community of machine learning since it can improve the performance of the model in a new domain (target domain) by borrowing knowledge from a labeled source domain. At the same time, the presence of large-scale labeled datasets also raised significant attention in this scenario: the class labels in the new domain are only a subset of those in the source domain. We propose an adversarial-net-based method in this paper, called domain compensatory adversarial network (DCAN). The critical difficulty of this problem is to reduce the negative impact of source instances with weak discriminability and filter out outlier source classes by exploiting the prior probability of classes. DCAN can identify source instances with weak discriminability and reverse its domain label to compensate for the target label space, which alleviates the negative effect of mismatching label space. Besides, DCAN reweights outlier source classes with the class prior distributions of source data for both category classifier and domain classifier to promote positive transfer. Experiments have revealed the superiority of DCAN compared to the existing methods.

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Acknowledgements

The work is supported by National Key R&D Program of China (2018YFC030940 0), National Natural Science Foundation of China (61871188), Guangzhou city science and technology research projects(201902020008).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, GuangZhou, China

    Junchu Huang, Pengyu Zhang & Zhiheng Zhou

  2. Key Laboratory of Big Data and Intelligent Robot, (South China University of Technology), Ministry of Education, GuangZhou, China

    Zhiheng Zhou

  3. China Electronics Standardization Institute, Beijing, 100007, China

    Kefeng Fan

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  1. Junchu Huang
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  2. Pengyu Zhang
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  3. Zhiheng Zhou
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  4. Kefeng Fan
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Correspondence to Zhiheng Zhou.

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Huang, J., Zhang, P., Zhou, Z. et al. Domain compensatory adversarial networks for partial domain adaptation. Multimed Tools Appl 80, 11255–11272 (2021). https://doi.org/10.1007/s11042-020-10193-0

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