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arXiv · 2510.14634

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

Jihyun Yu·Yoojin Oh·Wonho Bae·Mingyu Kim·Junhyug Noh
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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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Jihyun Yu, Yoojin Oh, Wonho Bae, Mingyu Kim, Junhyug Noh. 2025-10-16. SteeringTTA: Guiding Diffusion Trajectories for Robust Test-Time-Adaptation. https://arxiv.org/abs/2510.14634

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