  Deep neural networks are often overparameterized and may not easily achieve
model generalization. Adversarial training has shown effectiveness in improving
generalization by regularizing the change of loss on top of adversarially
chosen perturbations. The recently proposed sharpness-aware minimization (SAM)
algorithm conducts adversarial weight perturbation, encouraging the model to
converge to a flat minima. SAM finds a common adversarial weight perturbation
per-batch. Although per-instance adversarial weight perturbations are stronger
adversaries and can potentially lead to better generalization performance,
their computational cost is very high and thus it is impossible to use
per-instance perturbations efficiently in SAM. In this paper, we tackle this
efficiency bottleneck and propose sharpness-aware minimization with dynamic
reweighting (delta-SAM). Our theoretical analysis motivates that it is possible
to approach the stronger, per-instance adversarial weight perturbations using
reweighted per-batch weight perturbations. delta-SAM dynamically reweights
perturbation within each batch according to the theoretically principled
weighting factors, serving as a good approximation to per-instance
perturbation. Experiments on various natural language understanding tasks
demonstrate the effectiveness of delta-SAM.
