Domain generalization (DG) aims to learn models that can generalize well to unseen domains by training only on a set of source domains. Sharpness-Aware Minimization (SAM) has been a popular approach for this, aiming to find flat minima in the total loss landscape. However, we show that minimizing the total loss sharpness does not guarantee sharpness across individual domains. In particular, SAM can converge to fake flat minima, where the total loss may exhibit flat minima, but sharp minima are present in individual domains. Moreover, the current perturbation update in gradient ascent steps is ineffective in directly updating the sharpness of individual domains. Motivated by these findings, we introduce a novel DG algorithm, Decreased-overhead Gradual Sharpness-Aware Minimization (DGSAM), that applies gradual domain-wise perturbation to reduce sharpness consistently across domains while maintaining computational efficiency. Our experiments demonstrate that DGSAM outperforms state-of-the-art DG methods, achieving improved robustness to domain shifts and better performance across various benchmarks, while reducing computational overhead compared to SAM.

翻译：领域泛化（DG）旨在通过仅在多个源域上训练，使模型能够良好地泛化到未见过的目标域。锐度感知最小化（SAM）是解决该问题的常用方法，其目标是在总体损失曲面上寻找平坦最小值。然而，我们发现最小化总体损失锐度并不能保证各个域上的锐度均得到优化。具体而言，SAM可能收敛至虚假平坦最小值——此时总体损失可能呈现平坦最小值，但在单个域上仍存在尖锐最小值。此外，当前梯度上升步骤中的扰动更新无法有效直接优化单个域的锐度。基于这些发现，我们提出了一种新颖的领域泛化算法——降开销渐进式锐度感知最小化（DGSAM），该算法通过渐进式逐域扰动在保持计算效率的同时，持续降低各域的锐度。实验表明，DGSAM在多个基准测试中优于当前最先进的领域泛化方法，不仅提升了模型对领域偏移的鲁棒性，获得了更优的性能，同时相比SAM降低了计算开销。