This paper presents a Domain-Inspired Sharpness-Aware Minimization (DISAM)
algorithm for optimization under domain shifts. It is motivated by the
inconsistent convergence degree of SAM across different domains, which induces
optimization bias towards certain domains and thus impairs the overall
convergence. To address this issue, we consider the domain-level convergence
consistency in the sharpness estimation to prevent the overwhelming (deficient)
perturbations for less (well) optimized domains. Specifically, DISAM introduces
the constraint of minimizing variance in the domain loss, which allows the
elastic gradient calibration in perturbation generation: when one domain is
optimized above the averaging level \textit{w.r.t.} loss, the gradient
perturbation towards that domain will be weakened automatically, and vice
versa. Under this mechanism, we theoretically show that DISAM can achieve
faster overall convergence and improved generalization in principle when
inconsistent convergence emerges. Extensive experiments on various domain
generalization benchmarks show the superiority of DISAM over a range of
state-of-the-art methods. Furthermore, we show the superior efficiency of DISAM
in parameter-efficient fine-tuning combined with the pretraining models. The
source code is released at this https URL

该论文提出了一种受领域启发的锐度感知极小化（Domain-Inspired Sharpness-Aware Minimization，DISAM）算法用于在领域变化下的优化问题。通过考虑锐度估计中的领域级收敛一致性，DISAM 引入了最小化领域损失方差的约束，实现了弹性梯度校准，在不同领域之间自动调整梯度扰动，从而实现更快的整体收敛和改进的泛化能力。在各种领域泛化基准测试中，大量实验证明了 DISAM 相比一系列最先进的方法的优越性。此外，我们还展示了 DISAM 在参数高效微调和预训练模型中的优越效率。