Domain generalization (DG) intends to train a model on multiple source domains to ensure that it can generalize well to an arbitrary unseen target domain. The acquisition of domain-invariant representations is pivotal for DG as they possess the ability to capture the inherent semantic information of the data, mitigate the influence of domain shift, and enhance the generalization capability of the model. Adopting multiple perspectives, such as the sample and the feature, proves to be effective. The sample perspective facilitates data augmentation through data manipulation techniques, whereas the feature perspective enables the extraction of meaningful generalization features. In this paper, we focus on improving the generalization ability of the model by compelling it to acquire domain-invariant representations from both the sample and feature perspectives by disentangling spurious correlations and enhancing potential correlations. 1) From the sample perspective, we develop a frequency restriction module, guiding the model to focus on the relevant correlations between object features and labels, thereby disentangling spurious correlations. 2) From the feature perspective, the simple Tail Interaction module implicitly enhances potential correlations among all samples from all source domains, facilitating the acquisition of domain-invariant representations across multiple domains for the model. The experimental results show that Convolutional Neural Networks (CNNs) or Multi-Layer Perceptrons (MLPs) with a strong baseline embedded with these two modules can achieve superior results, e.g., an average accuracy of 92.30% on Digits-DG.

翻译：域泛化旨在利用多个源域训练模型，使其能够很好地泛化到任意未见的目标域。获取域不变表示对域泛化至关重要，因为它能捕捉数据内在的语义信息、缓解域偏移的影响并提升模型泛化能力。采用多视角方法（如样本视角和特征视角）被证明是有效的：样本视角通过数据操作技术促进数据增强，而特征视角则能提取有意义的泛化特征。本文聚焦于通过解耦虚假相关性和增强潜在相关性，从样本与特征双视角迫使模型学习域不变表示以提升泛化能力：1）在样本视角，我们设计频率限制模块，引导模型关注目标特征与标签间的真实关联，从而解耦虚假相关性；2）在特征视角，简洁的尾部交互模块隐式增强所有源域样本间的潜在相关性，促进模型跨多域获取域不变表示。实验结果表明，嵌入这两个模块的强基线卷积神经网络或多层感知机均能取得优异结果，例如在Digits-DG数据集上平均准确率达92.30%。