Single domain generalization aims to learn a model from a single training domain (source domain) and apply it to multiple unseen test domains (target domains). Existing methods focus on expanding the distribution of the training domain to cover the target domains, but without estimating the domain shift between the source and target domains. In this paper, we propose a new learning paradigm, namely simulate-analyze-reduce, which first simulates the domain shift by building an auxiliary domain as the target domain, then learns to analyze the causes of domain shift, and finally learns to reduce the domain shift for model adaptation. Under this paradigm, we propose a meta-causal learning method to learn meta-knowledge, that is, how to infer the causes of domain shift between the auxiliary and source domains during training. We use the meta-knowledge to analyze the shift between the target and source domains during testing. Specifically, we perform multiple transformations on source data to generate the auxiliary domain, perform counterfactual inference to learn to discover the causal factors of the shift between the auxiliary and source domains, and incorporate the inferred causality into factor-aware domain alignments. Extensive experiments on several benchmarks of image classification show the effectiveness of our method.

翻译：单一领域泛化旨在从单一训练领域（源域）中学习模型，并将其应用于多个未见过的测试领域（目标域）。现有方法主要聚焦于扩展训练域的分布以覆盖目标域，但并未估计源域与目标域之间的域偏移。本文提出了一种名为“模拟-分析-缩减”的新型学习范式：首先通过构建辅助域作为目标域来模拟域偏移，然后学习分析域偏移的成因，最后学习如何缩减域偏移以实现模型自适应。在该范式下，我们提出了一种元因果学习方法，用于学习元知识——即训练过程中如何推断辅助域与源域之间域偏移的成因。在测试阶段，我们利用该元知识分析目标域与源域之间的偏移。具体而言，我们通过对源数据执行多重变换生成辅助域，通过反事实推断学习发现辅助域与源域偏移的因果因子，并将推断出的因果关联融入因子感知的域对齐中。在多个图像分类基准上的大量实验验证了本方法的有效性。