Oct 31, 2022 · Sharpness-Aware Minimization (SAM) is an emerging deep learning technology in recent years, which aims to find parameters lie in a "flat" region ...
We propose a novel random smoothing based SAM (R-SAM) algorithm. To be specific, R-SAM essentially smooths the loss landscape.
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What is sharpness-aware minimization?
Therefore, in this paper, we propose a novel random smoothing based sharpness-aware minimization algorithm (R-SAM). Our proposed R-SAM consists of two steps.
Apr 3, 2024 · Currently, Sharpness-Aware Minimization (SAM) is proposed to seek the parameters that lie in a flat region to improve the generalization ...
R-SAM essentially smooths the loss landscape, based on which it is able to apply the one-step gradient ascent on the smoothed weights to improve the ...
Sharpness-Aware Minimization (SAM) has been instru- mental in improving deep neural network training by min- imizing both training loss and loss sharpness.
Mar 18, 2022 · We propose a simple yet efficient training scheme, called Randomized Sharpness-Aware Training (RST). Optimizers in RST would perform a Bernoulli trial at each ...
Sharpness-Aware Minimization (SAM) is a recent training method that relies on worst-case weight perturbations which significantly improves gener-.
Oct 14, 2024 · Sharpness-aware minimization (SAM) has been shown to improve the generalization of neural networks. However, each SAM update requires ...
The papers are categorized into four groups: (1) improving efficiency of SAM (2) improving effectiveness of SAM (3) theoretical analysis of SAM (4) applications ...