Sharpness-Aware Minimization (SAM) has recently emerged as a robust technique for improving the accuracy of deep neural networks. However, SAM incurs a high computational cost in practice, requiring up to twice as much computation as vanilla SGD. The computational challenge posed by SAM arises because each iteration requires both ascent and descent steps and thus double the gradient computations. To address this challenge, we propose to compute gradients in both stages of SAM on only the top-k samples with highest loss. K-SAM is simple and extremely easy-to-implement while providing significant generalization boosts over vanilla SGD at little to no additional cost.
@article{arxiv.2210.12864,
title = {K-SAM: Sharpness-Aware Minimization at the Speed of SGD},
author = {Renkun Ni and Ping-yeh Chiang and Jonas Geiping and Micah Goldblum and Andrew Gordon Wilson and Tom Goldstein},
journal= {arXiv preprint arXiv:2210.12864},
year = {2022}
}