Towards Label-Noise Resistant Learning via Optimal Brain Damage Masking 事件

Towards Label-Noise Resistant Learning via Optimal Brain Damage Masking arXiv:2508.09697v3 Announce Type: replace-cross Abstract: Noisy labels are inevitable in real-world scenarios. Due to the strong capacity of deep neural networks to memorize corrupted labels, these noisy labels cause significant performance degradation. Existing noise-robust methods have mainly focused on robust loss functions and sample selection, with comparatively limited exploration of dynamic architectural adaptation.