  Sharpness-aware minimization (SAM) has well documented merits in enhancing
generalization of deep neural networks, even without sizable data augmentation.
Embracing the geometry of the loss function, where neighborhoods of 'flat
minima' heighten generalization ability, SAM seeks 'flat valleys' by minimizing
the maximum loss caused by an adversary perturbing parameters within the
neighborhood. Although critical to account for sharpness of the loss function,
such an 'over-friendly adversary' can curtail the outmost level of
generalization. The novel approach of this contribution fosters stabilization
of adversaries through variance suppression (VaSSO) to avoid such friendliness.
VaSSO's provable stability safeguards its numerical improvement over SAM in
model-agnostic tasks, including image classification and machine translation.
In addition, experiments confirm that VaSSO endows SAM with robustness against
high levels of label noise.
