Large-scale benchmark of noisy-label methods on frozen VFMs reveals no universal winner, with ELR and CUFIT performing differently, and demonstrates small-loss assumption failure via 53-61% loss overlap under asymmetric noise.
Early-learning regularization pre- vents memorization of noisy labels
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Rethinking Noise-Robust Training for Frozen Vision Foundation Models: A Cross-Dataset Benchmark with a Case Study of Small-Loss Failure
Large-scale benchmark of noisy-label methods on frozen VFMs reveals no universal winner, with ELR and CUFIT performing differently, and demonstrates small-loss assumption failure via 53-61% loss overlap under asymmetric noise.