Active Domain Adaptation Under Concept Shift

Active Domain Adaptation (ADA) enhances transfer learning by selecting the most informative samples from the target domain for annotation. The source-free variant, ASFDA, operates using only a pre-trained source model and unlabeled target data. However, existing approaches typically assume that distribution shift arises solely from changes in the feature or label distribution, thereby neglecting concept shift (the divergence of $p(y|x)$ across domains). In this paper, we propose ADA-CS, a plug-and-play module compatible with any ADA or ASFDA framework. Without requiring access to source data, ADA-CS isolates concept shift from other forms of shifts. The concept shift severity is quantified to prioritize samples exhibiting the largest discrepancy in $p(y|x)$ for annotation. We further introduce a CSS metric to quantify the Concept Shift Severity across domains, revealing that non-negligible concept shift exists in many transfer tasks. Extensive experiments on four vision benchmarks (Office-31, Digits, DomainNet, and Office-Home RSUT) and one large-scale tabular dataset (USAccident) show that ADA-CS improves performance when combined with a wide range of active learning, ADA, and ASFDA strategies. Moreover, the rapid decline of CSS during adaptation provides direct evidence of our approach's effectiveness in identifying and correcting concept shift.