Label-Noise Resistant Learning via Optimal Brain Damage Masking

תקציר מקורי באנגליתarXiv:2508.09697v4 Announce Type: replace Abstract: Noisy labels are inevitable in real-world multimedia applications. Due to the strong memorization capacity of deep neural networks, these noisy labels cause significant performance degradation. Existing noise-robust methods have mainly focused on robust loss functions and sample selection strategies, with comparatively limited exploration of dynamic architectural adaptation. In this paper, we rethink the role of classifier connectivity under label noise. Intuitively, performance degradation stems from the backpropagation of noisy gradients. Since the final classifier layer acts as the primary gateway for this error propagation, selectively discarding redundant connections can restrict the backpropagation pathways of noisy gradients. Conse