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：在本文件中,我们提议通过提供一个名为变异分解网络(VDN)的有效框架,解决域化问题,这个框架能够将特定领域的特点和具体任务的特点区分开来,预期具体任务的特点将更普遍地推广到不可见但相关的测试数据中。我们进一步证明,我们提议的框架相当于最大限度地减少特定任务特征分布与从变异推论中得出的无差异地面真相之间的差别的证据,从而表明我们拟议方法的理由。我们进行了广泛的实验,以核实我们关于三个基准的方法,而定量和定性结果都表明了我们方法的有效性。