Domain generalization (DG) seeks to learn robust models that generalize well under unknown distribution shifts. As a critical aspect of DG, optimizer selection has not been explored in depth. Currently, most DG methods follow the widely used benchmark, DomainBed, and utilize Adam as the default optimizer for all datasets. However, we reveal that Adam is not necessarily the optimal choice for the majority of current DG methods and datasets. Based on the perspective of loss landscape flatness, we propose a novel approach, Flatness-Aware Minimization for Domain Generalization (FAD), which can efficiently optimize both zeroth-order and first-order flatness simultaneously for DG. We provide theoretical analyses of the FAD's out-of-distribution (OOD) generalization error and convergence. Our experimental results demonstrate the superiority of FAD on various DG datasets. Additionally, we confirm that FAD is capable of discovering flatter optima in comparison to other zeroth-order and first-order flatness-aware optimization methods.

基于损失平面平坦度的角度，我们提出了一种新颖的方法Flatness-Aware Minimization for Domain Generalization（FAD），可以同时有效地优化零阶和一阶平坦度，从而改善领域泛化问题。我们通过理论分析和实验证明了FAD在各种领域泛化数据集上的优越性，并确认FAD能够发现比其他零阶和一阶平坦度感知优化方法更平坦的极小值点。

面向域一般化的平坦性感知最小化