Distilling Image Prototypes for Guided Test-Time Adaptation
Test-Time Adaptation (TTA) enhances the robustness of models against distribution shifts but faces two critical challenges: error accumulation from noisy pseudo-labels and catastrophic forgetting of source knowledge. Uncertainty-based approaches designed to mitigate error accumulation often yield overconfident or computationally expensive estimates, while strategies intended to prevent forgetting via prototype replay rely on static representations that easily become misaligned as the model adapts. To address these issues, this paper proposes a novel framework, Distilling Image Prototype for Guided Test-Time Adaptation (DIPTTA). The core of the proposed approach is the introduction of a Di...
| Authors | Liwen Wang, Xingbo Dong, Iman Yi Liao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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