RDumb: A simple approach that questions our progress in continual test-time adaptation
This work questions the progress in continual test-time adaptation for machine learning practitioners, revealing that many advanced methods are ineffective compared to a simplistic approach, indicating incremental improvements may be overstated.
The paper tackles the problem of performance collapse in continual test-time adaptation (TTA) methods by introducing the Continually Changing Corruptions (CCC) benchmark and finding that most state-of-the-art methods eventually perform worse than a non-adapting model, while a simple baseline called RDumb, which periodically resets the model, matches or outperforms them.
Test-Time Adaptation (TTA) allows to update pre-trained models to changing data distributions at deployment time. While early work tested these algorithms for individual fixed distribution shifts, recent work proposed and applied methods for continual adaptation over long timescales. To examine the reported progress in the field, we propose the Continually Changing Corruptions (CCC) benchmark to measure asymptotic performance of TTA techniques. We find that eventually all but one state-of-the-art methods collapse and perform worse than a non-adapting model, including models specifically proposed to be robust to performance collapse. In addition, we introduce a simple baseline, "RDumb", that periodically resets the model to its pretrained state. RDumb performs better or on par with the previously proposed state-of-the-art in all considered benchmarks. Our results show that previous TTA approaches are neither effective at regularizing adaptation to avoid collapse nor able to outperform a simplistic resetting strategy.