Self-Ensemble Post Learning for Noisy Domain Generalization

Distribution shift and label noise remain major challenges to model robustness. In domain generalization (DG), noisy source labels can impair performance on unseen domains. We observe that intermediate representations retain discriminative information and attend to different image regions. Based on these observations, we propose Self-Ensemble Post Learning (SEPL) to improve the robustness of existing DG methods. SEPL consists of two phases: feature probing training and prediction ensemble inference. It trains multiple classifiers on intermediate features from a frozen pretrained model and uses semi-supervised learning to mitigate label noise. Their predictions are then combined through a crowdsourcing inference approach that accounts for differences in probe reliability. Extensive experiments across image classification benchmarks and medical-image settings show that SEPL improves robustness under label noise across DG methods.