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 five widely used DG benchmark datasets including PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet. The proposed INSURE achieves state-of-the-art performance. We also empirically show that domain-specific class-relevant features are beneficial for domain generalization. The code is available at https://github.com/yuxi120407/INSURE .

中文翻译：

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INSURE：信息论启发的领域泛化解缠结和纯化模型


域泛化（DG）旨在通过仅在多个观察到的源域上进行训练来学习看不见的目标域上的可泛化模型。尽管各种 DG 方法都专注于提取域不变特征，但特定于域的类相关特征引起了人们的关注，并被认为有利于泛化到不可见的目标域。为了考虑到类相关的特定领域信息，在本文中，我们提出了一种信息论启发的解缠和纯化模型（INSURE），以明确地解开潜在特征，以获得足够且紧凑（必要的）类相关特征，以便泛化到看不见的领域。具体来说，我们首先提出一种信息论启发的损失函数，以确保解开的类相关特征包含足够的类标签信息，并且其他解开的辅助特征具有足够的领域信息。我们进一步提出了一种配对的净化损失函数，让辅助特征丢弃所有的类相关信息，因此类相关特征将包含足够且紧凑的（必要的）类相关信息。此外，我们建议使用可学习的二进制掩码作为我们的解缠器，而不是使用多个编码器，以使解缠更有效并使解缠的特征彼此互补。我们对五个广泛使用的 DG 基准数据集（包括 PACS、VLCS、OfficeHome、TerraIncognita 和 DomainNet）进行了广泛的实验。所提出的 INSURE 实现了最先进的性能。我们还凭经验表明，特定领域的类相关特征有利于领域泛化。该代码可在 https://github.com/yuxi120407/INSURE 获取。