SteeringTTA: Guiding Diffusion Trajectories for Robust Test-Time-Adaptation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918161759076352 |
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| author | Yu, Jihyun Oh, Yoojin Bae, Wonho Kim, Mingyu Noh, Junhyug |
| author_facet | Yu, Jihyun Oh, Yoojin Bae, Wonho Kim, Mingyu Noh, Junhyug |
| contents | Test-time adaptation (TTA) aims to correct performance degradation of deep models under distribution shifts by updating models or inputs using unlabeled test data. Input-only diffusion-based TTA methods improve robustness for classification to corruptions but rely on gradient guidance, limiting exploration and generalization across distortion types. We propose SteeringTTA, an inference-only framework that adapts Feynman-Kac steering to guide diffusion-based input adaptation for classification with rewards driven by pseudo-label. SteeringTTA maintains multiple particle trajectories, steered by a combination of cumulative top-K probabilities and an entropy schedule, to balance exploration and confidence. On ImageNet-C, SteeringTTA consistently outperforms the baseline without any model updates or source data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14634 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SteeringTTA: Guiding Diffusion Trajectories for Robust Test-Time-Adaptation Yu, Jihyun Oh, Yoojin Bae, Wonho Kim, Mingyu Noh, Junhyug Computer Vision and Pattern Recognition Test-time adaptation (TTA) aims to correct performance degradation of deep models under distribution shifts by updating models or inputs using unlabeled test data. Input-only diffusion-based TTA methods improve robustness for classification to corruptions but rely on gradient guidance, limiting exploration and generalization across distortion types. We propose SteeringTTA, an inference-only framework that adapts Feynman-Kac steering to guide diffusion-based input adaptation for classification with rewards driven by pseudo-label. SteeringTTA maintains multiple particle trajectories, steered by a combination of cumulative top-K probabilities and an entropy schedule, to balance exploration and confidence. On ImageNet-C, SteeringTTA consistently outperforms the baseline without any model updates or source data. |
| title | SteeringTTA: Guiding Diffusion Trajectories for Robust Test-Time-Adaptation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.14634 |