Orthogonal Gradient Constraints Shape Noisy-Label Memorization Dynamics
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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 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…
1Key Takeaways
- 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 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….
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv ML reports that 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.
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