Test-Time Adaptation
Overview
Test-time adaptation studies how pretrained models can adjust to changing data during deployment, improving reliability when target labels or retraining are unavailable. Common approaches use entropy minimization, pseudo-labels, normalization or feature-statistics alignment, and lightweight prompts, adapters, caches, or memory; gradient-free methods aim to reduce compute and memory costs. Work spans image corruptions and domain shifts, medical imaging, time-series forecasting, language-model reasoning, and robotics. A central tension is that noisy self-generated signals can cause error accumulation or forgetting, so methods must determine when adaptation is useful and how to evaluate its benefits beyond average accuracy.
Latest papers
318 papers50 papers in the last four weeks, up 47% on the four weeks before. 0.3% of all new papers.