VASSO: Variance Suppression for Sharpness-Aware Minimization

Sharpness-aware minimization (SAM) has well-documented merits in enhancing generalization of deep neural network models. Accounting for sharpness in the loss function geometry, where neighborhoods of flat minima' heighten generalization ability, SAM seeks flat valleys’ by minimizing the maximum loss provoked by an adversarial perturbation within the neighborhood. Although critical to account for sharpness of the loss function, in practice SAM suffers from over-friendly adversaries,' which can curtail the outmost level of generalization. To avoid such friendliness,’ the present contribution fosters stabilization of adversaries through variance suppression (VASSO). VASSO offers a general approach to provably stabilize adversaries. In particular, when integrating VASSO with SAM, improved generalizability is numerically validated on extensive vision and language tasks. Once applied on top of a computationally efficient SAM variant, VASSO offers a desirable generalization-computation tradeoff.