Webb4 nov. 2024 · The sharpness of loss function can be defined as the difference between the maximum training loss in an ℓ p ball with a fixed radius ρ. and the training loss at w. The paper [1] shows the tendency that a sharp minimum has a larger generalization gap than a flat minimum does. WebbNext, we introduce the Sharpness-Aware Training for Free (SAF) algorithm whose pseudocode can be found in Algorithm 1. We first start with recalling SAM’s sharpness measure loss. Then we explain the intuition for the trajectory loss as a substitute for SAM’s sharpness measure loss in Section 3.1.

[2010.01412] Sharpness-Aware Minimization for Efficiently …

WebbIn this paper, we propose Sharpness-Aware Training for Free, or SAF, which mitigates the sharp landscape at almost zero additional computational cost over the base optimizer. … WebbSharpness-Aware Training for Free. Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations. Scalable design of Error-Correcting Output Codes using Discrete Optimization with Graph Coloring. dbs super hero film

Sharpness-Aware Training for Free

Webb15 mars 2024 · Recently, sharpness-aware minimization (SAM) establishes a generic scheme for generalization improvements by minimizing the sharpness measure within a small neighborhood and achieves... WebbIn this paper, we propose Sharpness-Aware Training for Free, or SAF, which mitigates the sharp landscape at almost zero additional computational cost over the base optimizer. Intuitively, SAF achieves this by avoiding sudden drops in the loss in the sharp local minima throughout the trajectory of the updates of the weights. WebbThe Sharpness Measure is defined as Objective:To find a “cheaper” replacement of the sharpness measure. where where is the past trajectory of the weights Then •Now, we … dbs super heroes streaming