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CLDA: an adversarial unsupervised domain adaptation method with classifier-level adaptation

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Abstract

Domain adaptation is an active and important research field in transfer learning. Unsupervised domain adaptation, which is better in line with real-world scenarios than supervised and semi-supervised domain adaptation, has attracted much attention and research. Inspired by generative adversarial networks (GANs), adversarial unsupervised domain adaptation methods are proposed in recent years, which are shown to achieve state-of-the-art performance. Existing adversarial unsupervised domain adaptation methods generally adopt feature-level adaptation to reduce the cross-domain shifts, which is shown to have some limitations in related research. In this paper, we propose a classifier-level adaptation approach to further reducing the cross-domain shifts. The classifier-level adaptation uses two different but related classifiers for source domain and target domain, different from existing adversarial unsupervised domain adaptation methods. In addition, not only domain-invariant feature representations but also auxiliary information of class labels is used to exploit the joint distribution of category information and extracted features. Based on the above-mentioned approaches, a classifier-level domain adaptation (CLDA) method is proposed. Experimental results show that the proposed CLDA method outperforms state-of-the-art unsupervised domain adaptation methods on Digits and Office-31 datasets.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (Project No. 61977013), Sichuan Science and Technology Program (Project No. 2019YJ0164), and Big Data Application on Improving Government Governance Capabilities National Engineering Laboratory Open Fund Project (Contract No. w-2019006). The authors would like to thank Mr. Haodong Liu, an M. Eng. candidate in School of Computer Science and Engineering, University of Electronic Science and Technology of China, for his help in conducting some of the experiments during the revision process of this paper.

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, People’s Republic of China

    Zhihai He, Bo Yang & Chaoxian Chen

  2. Big Data Application on Improving Government Governance Capabilities National Engineering Laboratory, Guiyang, 550022, People’s Republic of China

    Zhihai He, Qilin Mu & Zesong Li

  3. CETC Big Data Research Institute Co., Ltd., Guiyang, 550022, People’s Republic of China

    Zhihai He & Qilin Mu

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  1. Zhihai He
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  2. Bo Yang
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  3. Chaoxian Chen
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  4. Qilin Mu
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  5. Zesong Li
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Correspondence to Bo Yang.

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He, Z., Yang, B., Chen, C. et al. CLDA: an adversarial unsupervised domain adaptation method with classifier-level adaptation. Multimed Tools Appl 79, 33973–33991 (2020). https://doi.org/10.1007/s11042-020-08877-8

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