SteeringTTA: Guiding Diffusion Trajectories for Robust Test-Time-Adaptation

• URL: http://arxiv.org/abs/2510.14634v1
• Date: Thu, 16 Oct 2025 12:46:53 GMT
• Title: SteeringTTA: Guiding Diffusion Trajectories for Robust Test-Time-Adaptation
• Authors: Jihyun Yu, Yoojin Oh, Wonho Bae, Mingyu Kim, Junhyug Noh,
• Abstract summary: Test-time adaptation (TTA) aims to correct performance degradation of deep models under distribution shifts by updating models or inputs using unlabeled test data.<n>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.
• Score: 10.159672026403097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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