Prototypical class-wise test-time adaptation

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Test-time adaptation (TTA) refines pre-trained models during deployment, enabling them to effectively manage new, previously unseen data. However, existing TTA methods focus mainly on global domain alignment, which reduces domain-level gaps but often leads to suboptimal performance. This is because they fail to explicitly consider class-wise alignment, resulting in errors when reliable pseudo-labels are unavailable and source domain samples are inaccessible. In this study, we propose a prototypical class-wise test-time adaptation method, which consists of class-wise prototype adaptation and reliable pseudo-labeling. A main challenge in this approach is the lack of direct access to source domain samples. We leverage the class-specific knowledge contained in the weights of the pre-trained model. To construct class prototypes from the unlabeled target domain, we further introduce a methodology to enhance the reliability of pseudo labels. Our method is adaptable to various models and has been extensively validated, consistently outperforming baselines across multiple benchmark datasets.