Prototypical class-wise test-time adaptation
Pattern Recognition Letters, ISSN: 0167-8655, Vol: 187, Page: 49-55
2025
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
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.
Bibliographic Details
Elsevier BV
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