arxivcs.CV2026-07-20
Leveraging Dissimilarity Invariance as a Robust Anchor for Learning with Noisy Labels
Deep learning models excel in visual recognition but suffer severe performance drops when training labels are corrupted by noise. Under label noise prior work cannot learn accurate similarities and thus misguide the learning process. In this paper, we uncover a complementary and…