Enhancing Sharpness-Aware Optimization Through Variance Suppression

• URL: http://arxiv.org/abs/2309.15639v3
• Date: Fri, 22 Dec 2023 11:15:44 GMT
• Title: Enhancing Sharpness-Aware Optimization Through Variance Suppression
• Authors: Bingcong Li, Georgios B. Giannakis
• Abstract summary: This work embraces the geometry of the loss function, where neighborhoods of 'flat minima' heighten generalization ability. It seeks 'flat valleys' by minimizing the maximum loss caused by an adversary perturbing parameters within the neighborhood. Although critical to account for sharpness of the loss function, such an 'over-friendly adversary' can curtail the outmost level of generalization.
• Score: 48.908966673827734
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Sharpness-aware minimization (SAM) has well documented merits in enhancing generalization of deep neural networks, even without sizable data augmentation. Embracing the geometry of the loss function, where neighborhoods of 'flat minima' heighten generalization ability, SAM seeks 'flat valleys' by minimizing the maximum loss caused by an adversary perturbing parameters within the neighborhood. Although critical to account for sharpness of the loss function, such an 'over-friendly adversary' can curtail the outmost level of generalization. The novel approach of this contribution fosters stabilization of adversaries through variance suppression (VaSSO) to avoid such friendliness. VaSSO's provable stability safeguards its numerical improvement over SAM in model-agnostic tasks, including image classification and machine translation. In addition, experiments confirm that VaSSO endows SAM with robustness against high levels of label noise.

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