mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization

• URL: http://arxiv.org/abs/2302.09693v2
• Date: Sun, 1 Oct 2023 02:19:50 GMT
• Title: mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
• Authors: Kayhan Behdin, Qingquan Song, Aman Gupta, Sathiya Keerthi, Ayan Acharya, Borja Ocejo, Gregory Dexter, Rajiv Khanna, David Durfee, Rahul Mazumder
• Abstract summary: Sharpness-Aware Minimization technique modifies the fundamental loss function that steers gradient descent methods toward flatter minima. We extend a recently developed and well-studied general framework for flatness analysis to theoretically show that SAM achieves flatter minima than SGD, and mSAM achieves even flatter minima than SAM.
• Score: 20.560184120992094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance. The Sharpness-Aware Minimization (SAM) technique modifies the fundamental loss function that steers gradient descent methods toward flatter minima, which are believed to exhibit enhanced generalization prowess. Our study delves into a specific variant of SAM known as micro-batch SAM (mSAM). This variation involves aggregating updates derived from adversarial perturbations across multiple shards (micro-batches) of a mini-batch during training. We extend a recently developed and well-studied general framework for flatness analysis to theoretically show that SAM achieves flatter minima than SGD, and mSAM achieves even flatter minima than SAM. We provide a thorough empirical evaluation of various image classification and natural language processing tasks to substantiate this theoretical advancement. We also show that contrary to previous work, mSAM can be implemented in a flexible and parallelizable manner without significantly increasing computational costs. Our implementation of mSAM yields superior generalization performance across a wide range of tasks compared to SAM, further supporting our theoretical framework.

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