[Submitted on 24 Jun 2026]
Title:Orthogonal Gradient Constraints Shape Noisy-Label Memorization Dynamics
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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 intervention on the optimizer update itself. We evaluate OrthoGrad, which removes the component of each weight gradient parallel to the current weight vector, in noisy-label image classification. On MNIST with small-data regimes, OrthoGrad improves test accuracy most clearly for CNNs while reducing corrupted-label fitting. Mechanism diagnostics based on weight norms and gradient-weight cosine similarity suggest that the projection has the strongest effect when the raw gradient contains a nontrivial radial component, and becomes weaker in larger-data regimes where gradients are already nearly orthogonal to weights. Additional CIFAR-10 ResNet-18 experiments show that the method can alter memorization trajectories but does not prevent eventual noisy-label memorization. These results support orthogonal update constraints as a useful diagnostic for studying learning dynamics, while showing that OrthoGrad is regime-dependent rather than universally regularizing.
Comments: 7 pages, accepted into the ICML HiLD workshop 2026 @Seoul
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2607.16231 [cs.LG]
(or arXiv:2607.16231v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.16231
arXiv-issued DOI via DataCite
Submission history
From: Richard Mai [view email] [v1] Wed, 24 Jun 2026 04:34:31 UTC (3,067 KB)
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