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Domain-Specific Bias Filtering for Single Labeled Domain Generalization

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Abstract

Conventional Domain Generalization (CDG) utilizes multiple labeled source datasets to train a generalizable model for unseen target domains. However, due to expensive annotation costs, the requirements of labeling all the source data are hard to be met in real-world applications. In this paper, we investigate a Single Labeled Domain Generalization (SLDG) task with only one source domain being labeled, which is more practical and challenging than the CDG task. A major obstacle in the SLDG task is the discriminability-generalization bias: the discriminative information in the labeled source dataset may contain domain-specific bias, constraining the generalization of the trained model. To tackle this challenging task, we propose a novel framework called Domain-Specific Bias Filtering (DSBF), which initializes a discriminative model with the labeled source data and then filters out its domain-specific bias with the unlabeled source data for generalization improvement. We divide the filtering process into (1) feature extractor debiasing via k-means clustering-based semantic feature re-extraction and (2) classifier rectification through attention-guided semantic feature projection. DSBF unifies the exploration of the labeled and the unlabeled source data to enhance the discriminability and generalization of the trained model, resulting in a highly generalizable model. We further provide theoretical analysis to verify the proposed domain-specific bias filtering process. Extensive experiments on multiple datasets show the superior performance of DSBF in tackling both the challenging SLDG task and the CDG task.

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Acknowledgements

This work was supported in part by National Key Research and Development Program of China (2021YFC3340300), National Natural Science Foundation of China (U20A20387, Nos. 62006207, 62037001), Young Elite Scientists Sponsorship Program by CAST(2021QNRC001), Project by Shanghai AI Laboratory (P22KS00111), the Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study (SN-ZJU-SIAS-0010), Natural Science Foundation of Zhejiang Province (LZ22F020012, LQ21F020020), Fundamental Research Funds for the Central Universities (226-2022-00142, 226-2022-00051), National Key Research and Development Project (2022YFC2504605).

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  1. Junkun Yuan and Xu Ma have contributed equally to this work.

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Junkun Yuan, Xu Ma, Defang Chen, Kun Kuang, Fei Wu & Lanfen Lin

  2. Shanghai Institute for Advanced Study of Zhejiang University, Shanghai, China

    Fei Wu

  3. Shanghai AI Laboratory, Shanghai, China

    Fei Wu

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  1. Junkun Yuan
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Correspondence to Kun Kuang.

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Yuan, J., Ma, X., Chen, D. et al. Domain-Specific Bias Filtering for Single Labeled Domain Generalization. Int J Comput Vis 131, 552–571 (2023). https://doi.org/10.1007/s11263-022-01712-7

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