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

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. In this paper, we rethink the role of model connectivity in the presence of label noise. Intuitively, performance degradation caused by noisy labels stems from the backpropagation of noisy gradients. Since the final classifier layer acts as the primary gateway for this error propagation, directly discarding redundant connections within the classifier can structurally intercept noisy gradients at the root.