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Adaptive prototype and consistency alignment for semi-supervised domain adaptation

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Abstract

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a label-rich source domain to an unlabeled target domain whose data distributions are different. There is a more realistic scenario where a few target labels are available, namely Semi-Supervised Domain Adaptation (SSDA). The existing methods reduce the inter-domain discrepancy by ignoring the class-level information, which may lead to cross-domain feature mismatch. Therefore, the model fails to learn discriminative feature representation for the target domain. In this paper, we propose a novel SSDA method, namely Adaptive Prototype and Consistency Alignment (APCA). To be specific, the Adaptive Prototype Alignment (APA) strategy employs a novel prototypical loss to realize the class-level alignment and further reduce the inter-domain discrepancy. Moreover, we apply Consistency Alignment (CA) to improve the robustness of the model and produce a robust cluster core which is beneficial to class-level alignment and thus facilitates the reduction of inter-domain discrepancy. We evaluate our approach on four domain adaptation datasets and the experimental results demonstrate the effectiveness of our proposed approach.

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Data Availibility Statement

The datasets analyzed during the current study are available in the [DomainNet] repository [40]; the [Office-Home] repository [52]; the [Office31] repository [43]; and the [VisDAT2017] repository [41].

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.62276113). Fund receiver: Dr. Ximing Li. This work is funded by the University of Economics Ho Chi Minh City (UEH), Vietnam. Fund receiver: Dr. Dang Ngoc Hoang Thanh.

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Authors and Affiliations

  1. College of Computer Science and Technolog, Jilin University, Jilin, China

    Jihong Ouyang, Zhengjie Zhang, Qingyi Meng & Ximing Li

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

    Jihong Ouyang, Zhengjie Zhang, Qingyi Meng & Ximing Li

  3. College of Technology and Design, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Dang Ngoc Hoang Thanh

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  1. Jihong Ouyang
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  2. Zhengjie Zhang
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Correspondence to Ximing Li or Dang Ngoc Hoang Thanh.

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Ouyang, J., Zhang, Z., Meng, Q. et al. Adaptive prototype and consistency alignment for semi-supervised domain adaptation. Multimed Tools Appl 83, 9307–9328 (2024). https://doi.org/10.1007/s11042-023-15749-4

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