Domain Generalization (DG) aims to learn a generalizable model on the unseen target domain by only training on the multiple observed source domains. Although a variety of DG methods have focused on extracting domain-invariant features, the domain-specific class-relevant features have attracted attention and been argued to benefit generalization to the unseen target domain. To take into account the class-relevant domain-specific information, in this paper we propose an Information theory iNspired diSentanglement and pURification modEl (INSURE) to explicitly disentangle the latent features to obtain sufficient and compact (necessary) class-relevant feature for generalization to the unseen domain. Specifically, we first propose an information theory inspired loss function to ensure the disentangled class-relevant features contain sufficient class label information and the other disentangled auxiliary feature has sufficient domain information. We further propose a paired purification loss function to let the auxiliary feature discard all the class-relevant information and thus the class-relevant feature will contain sufficient and compact (necessary) class-relevant information. Moreover, instead of using multiple encoders, we propose to use a learnable binary mask as our disentangler to make the disentanglement more efficient and make the disentangled features complementary to each other. We conduct extensive experiments on four widely used DG benchmark datasets including PACS, OfficeHome, TerraIncognita, and DomainNet. The proposed INSURE outperforms the state-of-art methods. We also empirically show that domain-specific class-relevant features are beneficial for domain generalization.

翻译：领域泛化（Domain Generalization，DG）旨在仅通过多个观测到的源域训练，学习一个适用于未见目标域的泛化模型。尽管已有多种DG方法聚焦于提取域不变特征，但域特定类别相关特征近年来受到关注，并被论证有助于对未见目标域的泛化。为充分利用类别相关的域特定信息，本文提出一种受信息论启发的解耦与纯化模型（INSURE），通过显式解耦潜在特征，为向未见域泛化获取充分且紧凑（必要）的类别相关特征。具体而言，我们首先提出一种受信息论启发的损失函数，确保解耦后的类别相关特征包含充分的类别标签信息，而另一解耦得到的辅助特征则包含充分的域信息。进一步地，我们提出一种配对纯化损失函数，使辅助特征丢弃所有类别相关信息，从而使类别相关特征包含充分且紧凑（必要）的类别相关信息。此外，我们未使用多个编码器，而是引入可学习二值掩码作为解耦器，以提高解耦效率并确保解耦特征之间互补。我们在四个广泛使用的DG基准数据集（包括PACS、OfficeHome、TerraIncognita和DomainNet）上进行了大量实验。所提出的INSURE方法优于现有最先进方法。同时，我们通过实验证明，域特定的类别相关特征对领域泛化有积极作用。