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.

通过利用损失函数的几何特性，尤其是最小值邻域的平坦特性，锐度感知最小化（SAM）提高了深度神经网络的泛化能力，而无需大规模增强数据。本文的创新方法通过方差抑制（VaSSO）稳定了对手，并证明了在模型无关任务中，包括图像分类和机器翻译中，VaSSO 相对于 SAM 的数值性能得到了提高。此外，实验证实 VaSSO 使 SAM 对高水平的标签噪声具有鲁棒性。