When and Where to Reset Matters for Long-Term Test-Time Adaptation

Researchers propose an Adaptive and Selective Reset (ASR) scheme to address model collapse in long-term test-time adaptation, where AI models gradually degrade and predict only a few classes. The solution dynamically determines when and where to reset models while preserving beneficial knowledge through importance-aware regularization.