A Versatile Framework for Continual Test-Time Domain Adaptation: Balancing Discriminability and Generalizability

Continual test-time domain adaptation (CTTA) aims to adapt the source pre-trained model to a continually changing target domain without additional data acquisition or labeling costs. This issue necessitates an initial performance enhancement within the present domain without labels while concurrently averting an excessive bias toward the current domain. Such bias exacerbates catastrophic forgetting and diminishes the generalization ability to future domains. To tackle the problem, this paper designs a versatile framework to capture high-quality supervision signals from three aspects: 1) The adaptive thresholds are employed to determine the reliability of pseudo-labels; 2) The knowledge from the source pre-trained model is utilized to adjust the unreliable one, and 3) By evaluating past supervision signals, we calculate a diversity score to ensure subsequent generalization. In this way, we form a complete supervisory signal generation framework, which can capture the current domain discriminative and reserve generalization in future domains. Finally, to avoid catastrophic forgetting, we design a weighted soft parameter alignment method to explore the knowledge from the source model. Extensive experimental results demonstrate that our method performs well on several benchmark datasets.