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Causal view mechanism for adversarial domain adaptation

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Abstract

Studies show that the challenge for adversarial domain adaptation is learning domain-invariant representations and alleviating the domain gap. However, the construction of domain-invariant representations suppresses the class-level structure information, and the pursuit of class-level structure information distorts the constructed domain-invariant representations. Till present, it still has difficulty to capture the domain-invariant representations while preserving the class-level structure information in the adversarial training process explicitly. In this paper, we propose a Causal view Mechanism for adversarial Domain Adaptation (CMDA). Firstly, a causal effect model of adversarial DA is proposed and reveals the influence of potential confounders in the adversarial training process. Then, CMDA is proposed to disentangle the domain-specific representations into multiple underlying factors and filter out irrelevant confounding characteristics. Specifically, CMDA capture the desired domain-invariant representations by subtracting the domain-level and class-level confounding characteristics. CMDA not only could preserve the class-level structure information to reduce classification error, but also improve transferability simultaneously. Finally, experiments carried out on Office-31, Office-Home, and VisDA-2017 datasets show that our CMDA method presents strong competition among some recent domain adaptation methods, and the average accuracies achieve 71.3%, 89.3% and 76.1% respectively.

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Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Notes

  1. Code available at https://drive.google.com/drive/folders/1uJKHN5JfsVsM0dLIGBgx9P-DoIK2wllw?usp=sharing.

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Acknowledgements

This work is supported by the Innovation Capacity Construction Project of Jilin Province Development and Reform Commission(2021FGWCXNLJSSZ10,2019C053-3), the National Key Research and Development Program of China (No. 2020YFA0714103) and the Fundamental Research Funds for the Central Universities, JLU.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Zihao Fu, Shengsheng Wang, Xin Zhao, Sifan Long & Bilin Wang

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Zihao Fu, Shengsheng Wang, Xin Zhao, Sifan Long & Bilin Wang

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  1. Zihao Fu
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Correspondence to Shengsheng Wang.

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Fu, Z., Wang, S., Zhao, X. et al. Causal view mechanism for adversarial domain adaptation. Multimed Tools Appl 82, 47347–47366 (2023). https://doi.org/10.1007/s11042-023-15683-5

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