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Tiffany Vlaar

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Normalization Layers Are All That Sharpness-Aware Minimization Needs
Maximilian Müller
Tiffany Vlaar
David Rolnick
Matthias Hein
Sharpness-aware minimization (SAM) was proposed to reduce sharpness of minima and has been shown to enhance generalization performance in va… (voir plus)rious settings. In this work we show that perturbing only the affine normalization parameters (typically comprising 0.1% of the total parameters) in the adversarial step of SAM can outperform perturbing all of the parameters.This finding generalizes to different SAM variants and both ResNet (Batch Normalization) and Vision Transformer (Layer Normalization) architectures. We consider alternative sparse perturbation approaches and find that these do not achieve similar performance enhancement at such extreme sparsity levels, showing that this behaviour is unique to the normalization layers. Although our findings reaffirm the effectiveness of SAM in improving generalization performance, they cast doubt on whether this is solely caused by reduced sharpness.
2023-12-11
Conference on Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net