Domain generalization (DG) aims to tackle the distribution shift between training domains and unknown target domains. Generating new domains is one of the most effective approaches, yet its performance gain depends on the distribution discrepancy between the generated and target domains. Distributionally robust optimization is promising to tackle distribution discrepancy by exploring domains in an uncertainty set. However, the uncertainty set may be overwhelmingly large, leading to low-confidence prediction in DG. It is because a large uncertainty set could introduce domains containing semantically different factors from training domains. To address this issue, we propose to perform a $\textbf{mo}$derately $\textbf{d}$istributional $\textbf{e}$xploration (MODE) for domain generalization. Specifically, MODE performs distribution exploration in an uncertainty $\textit{subset}$ that shares the same semantic factors with the training domains. We show that MODE can endow models with provable generalization performance on unknown target domains. The experimental results show that MODE achieves competitive performance compared to state-of-the-art baselines.

翻译：域泛化旨在解决训练域与未知目标域之间的分布偏移问题。生成新域是最有效的方法之一，但其性能提升取决于生成域与目标域之间的分布差异。分布鲁棒优化通过在不确性集中探索域来处理分布差异，具有良好前景。然而，过大的不确定性集可能引入包含与训练域语义因素不同的域，导致域泛化中低置信度预测。针对此问题，我们提出对域泛化进行**适度分布探索**（MODE）。具体而言，MODE在与训练域共享相同语义因素的不确定性**子集**内进行分布探索。理论证明MODE能使模型在未知目标域上具有可验证的泛化性能。实验结果表明，与最先进的基线方法相比，MODE取得了具有竞争力的性能。