Unsupervised Domain Shift Detection via Discrepancy Between Pointwise and Prototype-Refined Pseudo-Labels

We study unsupervised domain-shift detection for image classifiers in the realistic setting where target labels are unavailable. We propose a simple detector based on the discrepancy between two label assignments on unlabeled target data: the classifier’s pointwise pseudo-labels and prototype-refined pseudo-labels obtained through batch-level prototype refinement in feature space. Our key observation is that these two labelings largely agree when the source classifier remains well aligned with the target domain, but diverge under harmful shift, when the source decision boundary becomes mismatched with the target structure. This leads to a shift detection score that can be computed directly from a deployed model, without training an auxiliary detector or explicitly estimating source–target divergence. Experiments on standard visual domain-shift benchmarks show that the proposed score accurately detects batch-level shift and tracks accuracy degradation better than confidence-based and distribution-based baselines. These results suggest that pseudo-label self-consistency provides a simple and effective criterion for detecting performance-relevant distribution shift.