Probabilistic Contrastive Test-Time Adaptation
Test-time adaptation (TTA) enhances generalization against out-of-distribution data during inference. Recent advances in TTA leverage some techniques such as contrastive learning and entropy minimization to improve the discriminability and robustness of models in target domains. However, existing me...
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| Vydané v: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1 - 5 |
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06.04.2025
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| Abstract | Test-time adaptation (TTA) enhances generalization against out-of-distribution data during inference. Recent advances in TTA leverage some techniques such as contrastive learning and entropy minimization to improve the discriminability and robustness of models in target domains. However, existing methods often overlook simultaneous distribution shifts of sample and label, such as long-tail distributions, and contrastive learning approaches may require substantial storage for sample pairs. In this paper, we propose a novel Probabilistic Contrastive Test-time Adaptation (PCTA) method based on Expectation Maximization (EM), which is used to estimate the von Mises Fisher (vMF) distribution of test samples to capture both sample distribution and class proportions. The estimated distributions are used for probabilistic contrastive learning to adapt feature representations and optimize classification through class-weighted entropy minimization. Experimental results show that PCTA significantly enhances the performance across various distribution shifts and outperforms state-of-the-art methods in different scenarios involving both sample and label shifts. Code is available at https://github.com/youlj109/PCTA. |
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| AbstractList | Test-time adaptation (TTA) enhances generalization against out-of-distribution data during inference. Recent advances in TTA leverage some techniques such as contrastive learning and entropy minimization to improve the discriminability and robustness of models in target domains. However, existing methods often overlook simultaneous distribution shifts of sample and label, such as long-tail distributions, and contrastive learning approaches may require substantial storage for sample pairs. In this paper, we propose a novel Probabilistic Contrastive Test-time Adaptation (PCTA) method based on Expectation Maximization (EM), which is used to estimate the von Mises Fisher (vMF) distribution of test samples to capture both sample distribution and class proportions. The estimated distributions are used for probabilistic contrastive learning to adapt feature representations and optimize classification through class-weighted entropy minimization. Experimental results show that PCTA significantly enhances the performance across various distribution shifts and outperforms state-of-the-art methods in different scenarios involving both sample and label shifts. Code is available at https://github.com/youlj109/PCTA. |
| Author | Lu, Jiabao Huang, Xiayuan You, Linjing |
| Author_xml | – sequence: 1 givenname: Linjing surname: You fullname: You, Linjing email: youlinjing2023@ia.ac.cn organization: Institute of Automation Chinese Academy of Sciences,Beijing,China – sequence: 2 givenname: Jiabao surname: Lu fullname: Lu, Jiabao email: lujb9921@mails.jlu.edu.cn organization: School of Communication Engineering Jilin University,Changchun,China – sequence: 3 givenname: Xiayuan surname: Huang fullname: Huang, Xiayuan email: xiayuan.huang@ia.ac.cn organization: Institute of Automation Chinese Academy of Sciences,Beijing,China |
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| Snippet | Test-time adaptation (TTA) enhances generalization against out-of-distribution data during inference. Recent advances in TTA leverage some techniques such as... |
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| SubjectTerms | Adaptation models Contrastive learning Distribution Shifts Entropy Heavily-tailed distribution Label Shifts Minimization Probabilistic Contrastive Learning Probabilistic logic Robustness Signal processing Signal processing algorithms Speech processing Test-Time Adaptation |
| Title | Probabilistic Contrastive Test-Time Adaptation |
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