WebSharpness-Aware Minimization (SAM) is a recent training method that relies on worst-case weight perturbations which significantly improves generalization in various settings. We argue that the existing justifications for the success of SAM which are based on a PAC-Bayes generalization bound and the idea of convergence to flat minima are incomplete. Web1 Feb 2024 · Abstract: Sharpness-Aware Minimization (SAM) is a highly effective regularization technique for improving the generalization of deep neural networks for various settings. However, the underlying working of SAM remains elusive because of various intriguing approximations in the theoretical characterizations.

Improved Deep Neural Network Generalization Using m-Sharpness-Aware …

Web7 Apr 2024 · In this work, we show that Sharpness-Aware Minimization (SAM), a recently proposed optimization procedure that encourages convergence to flatter minima, can substantially improve the generalization of language … Understanding the arXiv Identifier; Understanding the ORCID iD; Institutional … intel teach unit plan

Improved Deep Neural Network Generalization Using m-Sharpness …

Web7 Apr 2024 · Comparatively little work has been done to improve the generalization of these models through better optimization. In this work, we show that Sharpness-Aware … WebIn [6], the authors extended the result of sharpness [15] and integrated this concept as a part of the training process, namely the sharpness-aware minimization. In the paper, the author instead of directly solving the inner maximization loop, uses linear approximation to resolve the issue. Furthermore, the Web23 Feb 2024 · Sharpness-Aware Minimization (SAM) (F oret et al., 2024) is an optimization framework that builds on the observ ation that sharpness of the training loss correlates with the generalization ... john c hendricks