StableTTA: Improving Vision Model Performance by Training-free Test-Time Adaptation Methods
Ensemble methods improve predictive performance but often incur high memory and computational costs. We identify an aggregation instability induced by nonlinear projection and voting operations. To address both efficiency challenges and this inconsistency, we propose StableTTA, a training-free test-time adaptation method with two variants. StableTTA-I targets coherent-batch inference settings, where temporally or semantically adjacent observations are likely to belong to the same class. Examples include burst photography, video streams, robotics perception, and industrial inspection. Under coherent-batch inference, StableTTA-I substantially improves prediction consistency and accuracy thr...
| Authors | Zheng Li, Jerry Cheng, Huanying Helen Gu |
|---|---|
| Primary category | cs.CV |
| Published | 6 April 2026 |
| Last updated at source | 7 May 2026 |
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