Understanding the Robustness of Sharpness-Aware Minimization (SAM) to Label Noise

Sharpness-Aware Minimization (SAM) achieves significantly higher test accuracy than stochastic gradient descent (SGD) in the presence of random label noise, by preferentially upweighting the gradient contribution of clean examples during early training.