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Generalizing to unseen domains via PatchMix

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Abstract

Domain generalization (DG) aims to transfer knowledge learned from multiple source domains to unseen domains. One of the primary challenges hinders DG is the insufficient diversity of source domains, which hampers the model’s ability to learn to generalize. Traditional data augmentation methods, which fuse content, style, labels, etc., unable to effectively learn the global features from the source domains. In this paper, we present an innovative approach to domain generalization learning technique, called PatchMix, by stitching the patches of different source domains together to build domain-mixup samples. This approach helps the model to learn the common features of different source domains. Meanwhile, a domain discriminator is introduced to preserve the model’s ability to distinguish the source domains, which is proved to be helpful for the model to generalize to unseen domains. To our best knowledge, we are the first to unveil the equation that elucidates the correlation between the number of patches and the number of source domains. Our method, PatchMix, outperforms the current state-of-the-art (SOTA) on four benchmark datasets.

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Data availability

The data utilized in this research is derived from publicly available datasets, and there are no copyright or privacy concerns associated with their usage. The datasets used in this study can be accessed in [http://www.mediafire.com/file/7yv132lgn1v267r/vlcs.tar.gz/file, https://datasets.activeloop.ai/docs/ml/datasets/pacs-dataset/, https://www.hemanthdv.org/officeHomeDataset.html, https://ai.bu.edu/M3SDA/].

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Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities (No.2042023kf1033).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, 430072, Hubei, China

    Juncheng Yang, Wei Yu & Shijun Li

  2. National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, 430072, Hubei, China

    Zuchao Li

  3. School of Electronic Information Engineering, Henan Polytechnic Institute, Nanyang, 473000, Henan, China

    Juncheng Yang

  4. JD Health International Inc., Bei**g, China

    Chao Li

  5. JD Explore Academy, Bei**g, China

    Shuai **e

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  5. Wei Yu
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  6. Shijun Li
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Contributions

In the main manuscript text, JY and SL contributed as the primary authors. ZL was responsible for revising the entire manuscript. Figures 2–4 were prepared by CL and WY, while SX participated in the code changes. Additionally, all authors participated in reviewing the manuscript.

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Correspondence to Zuchao Li or Shijun Li.

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Communicated by P. Pala.

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Yang, J., Li, Z., Li, C. et al. Generalizing to unseen domains via PatchMix. Multimedia Systems 30, 31 (2024). https://doi.org/10.1007/s00530-023-01213-8

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