Conformal Uncertainty Indicator for Continual Test-Time Adaptation
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917913714229248 |
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| author | Lyu, Fan Zhao, Hanyu Shi, Ziqi Liu, Ye Hu, Fuyuan Zhang, Zhang Wang, Liang |
| author_facet | Lyu, Fan Zhao, Hanyu Shi, Ziqi Liu, Ye Hu, Fuyuan Zhang, Zhang Wang, Liang |
| contents | Continual Test-Time Adaptation (CTTA) aims to adapt models to sequentially changing domains during testing, relying on pseudo-labels for self-adaptation. However, incorrect pseudo-labels can accumulate, leading to performance degradation. To address this, we propose a Conformal Uncertainty Indicator (CUI) for CTTA, leveraging Conformal Prediction (CP) to generate prediction sets that include the true label with a specified coverage probability. Since domain shifts can lower the coverage than expected, making CP unreliable, we dynamically compensate for the coverage by measuring both domain and data differences. Reliable pseudo-labels from CP are then selectively utilized to enhance adaptation. Experiments confirm that CUI effectively estimates uncertainty and improves adaptation performance across various existing CTTA methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02998 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conformal Uncertainty Indicator for Continual Test-Time Adaptation Lyu, Fan Zhao, Hanyu Shi, Ziqi Liu, Ye Hu, Fuyuan Zhang, Zhang Wang, Liang Machine Learning Continual Test-Time Adaptation (CTTA) aims to adapt models to sequentially changing domains during testing, relying on pseudo-labels for self-adaptation. However, incorrect pseudo-labels can accumulate, leading to performance degradation. To address this, we propose a Conformal Uncertainty Indicator (CUI) for CTTA, leveraging Conformal Prediction (CP) to generate prediction sets that include the true label with a specified coverage probability. Since domain shifts can lower the coverage than expected, making CP unreliable, we dynamically compensate for the coverage by measuring both domain and data differences. Reliable pseudo-labels from CP are then selectively utilized to enhance adaptation. Experiments confirm that CUI effectively estimates uncertainty and improves adaptation performance across various existing CTTA methods. |
| title | Conformal Uncertainty Indicator for Continual Test-Time Adaptation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.02998 |