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Entropy minimization and domain adversarial training guided by label distribution similarity for domain adaptation

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Abstract

In domain adaptation, entropy minimization is widely used. However, entropy minimization will bring negative transfer when the pseudo-labels are inconsistent with the real labels. We hope to increase pseudo-label accuracy to counter negative transfer in entropy minimization. To this end, we introduce domain adversarial training into entropy minimization. Furthermore, we consider the misalignment caused by domain adversarial training under severe label shift. Therefore, we propose method called entropy minimization and domain adversarial training guided by label distribution similarity (EMALDS). Through domain adversarial training which focus more on class-aligned divergence, our method improves pseudo-label accuracy and reduce negative transfer in entropy minimization. Extensive experiments demonstrate the effectiveness and robustness of our proposed method.

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Data availability

The data that support the findings of this study are openly available in [9] at [https://engineering.case.edu/bearingdatacenter/download-data-file].

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Authors and Affiliations

  1. Department of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China

    Fangzheng Xu, Bingye Li, Zhining Hou & Lekang Wang

  2. Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, China University of Mining and Technology, Xuzhou, Jiangsu, China

    Yu Bao

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  1. Fangzheng Xu
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  3. Bingye Li
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  4. Zhining Hou
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  5. Lekang Wang
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Contributions

All the authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by FX and YB. Experimental code is completed by FX. Bl, ZH and LW are responsible for drawing and typesetting. The first draft of the manuscript was written by FX and revision is completed by YB. All the authors read and approved the final manuscript.

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Correspondence to Yu Bao.

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Appendix

Appendix

1.1 More experiment details on 1797–1730 of 12k FanEnd fault for partial domain adaptation

See Table 6.

Table 6 Performance metrics on 1797–1730 of 12k FanEnd Fault for partial domain adaptation (ResNet-18)
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Xu, F., Bao, Y., Li, B. et al. Entropy minimization and domain adversarial training guided by label distribution similarity for domain adaptation. Multimedia Systems 29, 2281–2292 (2023). https://doi.org/10.1007/s00530-023-01106-w

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