Domain generalization (DG) aims to learn models that perform well on unseen target domains by training on multiple source domains. Sharpness-Aware Minimization (SAM), known for finding flat minima that improve generalization, has therefore been widely adopted in DG. However, our analysis reveals that SAM in DG may converge to \textit{fake flat minima}, where the total loss surface appears flat in terms of global sharpness but remains sharp with respect to individual source domains. To understand this phenomenon more precisely, we formalize the average worst-case domain risk as the maximum loss under domain distribution shifts within a bounded divergence, and derive a generalization bound that reveals the limitations of global sharpness-aware minimization. In contrast, we show that individual sharpness provides a valid upper bound on this risk, making it a more suitable proxy for robust domain generalization. Motivated by these insights, we shift the DG paradigm toward minimizing individual sharpness across source domains. We propose \textit{Decreased-overhead Gradual SAM (DGSAM)}, which applies gradual domain-wise perturbations in a computationally efficient manner to consistently reduce individual sharpness. Extensive experiments demonstrate that DGSAM not only improves average accuracy but also reduces performance variance across domains, while incurring less computational overhead than SAM.

翻译：领域泛化（DG）旨在通过多个源域的训练，使模型在未见目标域上表现良好。以寻找能提升泛化能力的平坦最小值而闻名的锐度感知最小化（SAM）因此被广泛应用于DG。然而，我们的分析表明，DG中的SAM可能收敛至\textit{伪平坦最小值}——此时总损失曲面在全局锐度度量下看似平坦，但相对于各源域仍保持尖锐。为更精确理解此现象，我们将平均最坏情况领域风险形式化为有界散度内领域分布偏移下的最大损失，并推导出揭示全局锐度感知最小化局限性的泛化界。与之相对，我们证明个体锐度为此风险提供了有效的上界，使其成为鲁棒领域泛化更合适的代理目标。基于这些发现，我们将DG范式转向最小化跨源域的个体锐度。我们提出\textit{降开销渐进式SAM（DGSAM）}，该方法以计算高效的方式实施渐进式逐域扰动，持续降低个体锐度。大量实验表明，DGSAM不仅能提升平均准确率，还能降低跨域性能方差，同时比SAM产生更少的计算开销。