arXiv cs.LG
· Papers
Orthogonal Gradient Constraints Shape Noisy-Label Memorization Dynamics
arXiv:2607.16231v1 Announce Type: new Abstract: Modern neural networks can fit corrupted training labels, making noisy-label learning a useful setting for studying memorization-driven overfitting. Most regularization methods modify the objective, architecture, or data distribution; here we instead study a geometric int