Aiming to generalize the model trained in source domains to unseen target domains, domain generalization (DG) has attracted lots of attention recently. The key issue of DG is how to prevent overfitting to the observed source domains because target domain is unavailable during training. We investigate that overfitting not only causes the inferior generalization ability to unseen target domains but also leads unstable prediction in the test stage. In this paper, we observe that both sampling multiple tasks in training stage and generating augmented images in test stage largely benefit generalization performance. Thus, by treating tasks and images as different views, we propose a novel multi-view DG framework. Specifically, in training stage, to enhance generalization ability, we develop a multi-view regularized meta-learning algorithm that employs multiple tasks to produce a suitable optimization direction during updating model. In test stage, to alleviate unstable prediction, we utilize multiple augmented images to yield multi-view prediction, which significantly promotes model reliability via fusing the results of different views of a test image. Extensive experiments on three benchmark datasets validate our method outperforms several state-of-the-art approaches.

本文提出了一种基于多视角的领域泛化框架，利用多任务和多视角的元学习进行模型训练和更新，有效降低模型过拟合和不稳定预测问题，并在三个基准数据集上得到较好的结果，优于目前的一些最先进方法。

MVDG：用于域泛化的统一多视角框架