The Crucial Role of Normalization in Sharpness-Aware Minimization

• URL: http://arxiv.org/abs/2305.15287v2
• Date: Mon, 23 Oct 2023 16:12:38 GMT
• Title: The Crucial Role of Normalization in Sharpness-Aware Minimization
• Authors: Yan Dai, Kwangjun Ahn, Suvrit Sra
• Abstract summary: Sharpness-Aware Minimization (SAM) is a gradient-based neural network that greatly improves prediction performance. We argue that two properties of normalization make SAM robust against the choice of hyper- practicalitys.
• Score: 44.00155917998616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sharpness-Aware Minimization (SAM) is a recently proposed gradient-based optimizer (Foret et al., ICLR 2021) that greatly improves the prediction performance of deep neural networks. Consequently, there has been a surge of interest in explaining its empirical success. We focus, in particular, on understanding the role played by normalization, a key component of the SAM updates. We theoretically and empirically study the effect of normalization in SAM for both convex and non-convex functions, revealing two key roles played by normalization: i) it helps in stabilizing the algorithm; and ii) it enables the algorithm to drift along a continuum (manifold) of minima -- a property identified by recent theoretical works that is the key to better performance. We further argue that these two properties of normalization make SAM robust against the choice of hyper-parameters, supporting the practicality of SAM. Our conclusions are backed by various experiments.

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