Domain Generalization (DG) is a fundamental challenge for machine learning models, which aims to improve model generalization on various domains. Previous methods focus on generating domain invariant features from various source domains. However, we argue that the domain variantions also contain useful information, ie, classification-aware information, for downstream tasks, which has been largely ignored. Different from learning domain invariant features from source domains, we decouple the input images into Domain Expert Features and noise. The proposed domain expert features lie in a learned latent space where the images in each domain can be classified independently, enabling the implicit use of classification-aware domain variations. Based on the analysis, we proposed a novel paradigm called Domain Disentanglement Network (DDN) to disentangle the domain expert features from the source domain images and aggregate the source domain expert features for representing the target test domain. We also propound a new contrastive learning method to guide the domain expert features to form a more balanced and separable feature space. Experiments on the widely-used benchmarks of PACS, VLCS, OfficeHome, DomainNet, and TerraIncognita demonstrate the competitive performance of our method compared to the recently proposed alternatives.

翻译：域泛化（Domain Generalization，DG）是机器学习模型面临的一个基本挑战，旨在提升模型在不同域上的泛化能力。以往的方法侧重于从多个源域中生成域不变特征。然而，我们认为域变化也包含对下游任务有用的信息，即分类感知信息，而这在很大程度上被忽视了。与从源域中学习域不变特征不同，我们将输入图像解耦为域专家特征和噪声。所提出的域专家特征位于一个学习到的隐空间中，在该空间中每个域内的图像可以被独立分类，从而能够隐式地利用分类感知的域变化。基于此分析，我们提出了一种名为域解耦网络（Domain Disentanglement Network，DDN）的新范式，用于将域专家特征从源域图像中解耦出来，并聚合源域专家特征以表示目标测试域。我们还提出了一种新的对比学习方法，以引导域专家特征形成更均衡且可分离的特征空间。在广泛使用的基准数据集PACS、VLCS、OfficeHome、DomainNet和TerraIncognita上的实验表明，与近期提出的替代方法相比，我们的方法具有竞争力的性能。