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Uncertainty-guided joint unbalanced optimal transport for unsupervised domain adaptation

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Abstract

Unsupervised domain adaptation aims to improve the performance of model generalization on unlabeled target domain by utilizing given labeled training samples from source domain. The optimal transport theory has been widely used to reduce domain discrepancy. However, most existing optimal transport-based approaches inevitably have the pair-wise mismatching problem during alignment of feature distributions. Besides, most current studies tend to focus on the improvement of prediction accuracy while ignoring the uncertainty estimation of noisy training samples, which is particularly important for the learning of transferrable model. To alleviate these issues, we propose a framework, Uncertainty-guided Joint Unbalanced Optimal Transport (UJUOT), which employs a feature uncertainty estimation (FUE) mechanism and an unbalanced optimal transport strategy. FUE encodes uncertainty by modeling each image embedding as a Gaussian distribution, improving representation space with better inter-class separability and intra-class compactness. It not only makes it easier for the domain alignment, but also lets the model more robust to noisy data. In addition, to reduce negative transfer, we design a novel unbalanced optimal transport (UOT) strategy to achieve precise pair-wise matching, which fully utilizes discriminative class-aware information to learn mass adaptively in order to determine best transport plan. To our best knowledge, this is a pioneering work of introducing data uncertainty to unsupervised domain adaptation. Extensive experiments on various standard datasets prove that our proposal can significantly improve transfer performance, outperforming state-of-the-art methods in many aspects.

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Data availability

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No.61975048).

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Authors and Affiliations

  1. College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China

    Jun Dan, Tao Jin, Shunjie Dong & Yixuan Shen

  2. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China

    Hao Chi

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Dan, J., Jin, T., Chi, H. et al. Uncertainty-guided joint unbalanced optimal transport for unsupervised domain adaptation. Neural Comput & Applic 35, 5351–5367 (2023). https://doi.org/10.1007/s00521-022-07976-x

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