1st-Order Magic: Analysis of Sharpness-Aware Minimization

• URL: http://arxiv.org/abs/2411.01714v1
• Date: Sun, 03 Nov 2024 23:50:34 GMT
• Title: 1st-Order Magic: Analysis of Sharpness-Aware Minimization
• Authors: Nalin Tiwary, Siddarth Aananth,
• Abstract summary: Sharpness-Aware Minimization (SAM) is an optimization technique designed to improve generalization by favoring flatter loss minima. We find that more precise approximations of the proposed SAM objective degrade generalization performance. This highlights a gap in our understanding of SAM's effectiveness and calls for further investigation into the role of approximations in optimization.
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• Abstract: Sharpness-Aware Minimization (SAM) is an optimization technique designed to improve generalization by favoring flatter loss minima. To achieve this, SAM optimizes a modified objective that penalizes sharpness, using computationally efficient approximations. Interestingly, we find that more precise approximations of the proposed SAM objective degrade generalization performance, suggesting that the generalization benefits of SAM are rooted in these approximations rather than in the original intended mechanism. This highlights a gap in our understanding of SAM's effectiveness and calls for further investigation into the role of approximations in optimization.

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