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Learning cross-domain representations by vision transformer for unsupervised domain adaptation

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Abstract

Unsupervised Domain Adaptation (UDA) is a popular machine learning technique to reduce the distribution discrepancy among domains. Generally, most UDA methods utilize a deep Convolutional Neural Networks (CNNs) and a domain discriminator to learn a domain-invariant representation, but it does not equal to a discriminative domain-specific representation. Transformers (TRANS), which has been proved to be more robust to domain shift than CNNs, has gradually become a powerful alternative to CNNs in feature representation. On the other hand, the domain shift between the labeled source data and the unlabeled target data produces a significant amount of label noise, which needs a more robust connection between the source and target domain. This report proposes a simple yet effective UDA method for learning cross-domain representations by vision Transformers in a self-training manner. Unlike the conventional form of dividing an image into multiple non-overlapping patches, we proposed a novel method that aggregates both source domain labeled patches and target domain pseudo-labeled target patches. In addition, a cross-domain alignment loss is proposed to match the centroid of labeled source patches and pseudo-labeled target patches. Extensive experiments show that our proposed method achieves state-of-the-art (SOTA) results on several standard UDA benchmarks (90.5\(\%\) on ImageCLEF-DA, Office-31) by a transformers baseline model without any extra assistant networks.

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Data availability

The data that support the finddings of this study are openly available in the following websites:

ImageCLEF-DA dataset is available in: https://www.imageclef.org/2014/adaptation

Office-31 dataset is available in: https://www.amazon.com (have mentioned in paper)

Office-Home dataset is available in: https://www.hemanthdv.org/officeHomeDataset.html.

Notes

  1. https://www.imageclef.org/2014/adaptation.

  2. https://pytorch.org.

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  1. School of Physics and Optoelectronic, Guangdong University of Technology, Xiaoguwei street, Guangzhou, 510000, Guangdong, China

    Yifan Ye, Shuai Fu & Jing Chen

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Ye, Y., Fu, S. & Chen, J. Learning cross-domain representations by vision transformer for unsupervised domain adaptation. Neural Comput & Applic 35, 10847–10860 (2023). https://doi.org/10.1007/s00521-023-08269-7

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