Domain generalization aims to learn an invariant model that can generalize well to the unseen target domain. In this paper, we propose to tackle the problem of domain generalization by delivering an effective framework named Variational Disentanglement Network (VDN), which is capable of disentangling the domain-specific features and task-specific features, where the task-specific features are expected to be better generalized to unseen but related test data. We further show the rationale of our proposed method by proving that our proposed framework is equivalent to minimize the evidence upper bound of the divergence between the distribution of task-specific features and its invariant ground truth derived from variational inference. We conduct extensive experiments to verify our method on three benchmarks, and both quantitative and qualitative results illustrate the effectiveness of our method.

翻译：领域泛化旨在学习一个能够很好地泛化到未见目标领域的恒等模型。本文提出通过构建一个名为变分解耦网络（Variational Disentanglement Network, VDN）的有效框架来解决领域泛化问题。该框架能够解耦领域特定特征与任务特定特征，其中任务特定特征预期能更好地泛化到未见但相关的测试数据。我们进一步论证了所提方法的合理性，通过证明该框架等价于最小化任务特定特征分布与其变分推断导出的不变真值分布之间散度的证据上界。我们在三个基准数据集上开展了大量实验来验证该方法，定量与定性结果均表明了我们方法的有效性。