  To generalize the model trained in source domains to unseen target domains,
domain generalization (DG) has recently attracted lots of attention. Since
target domains can not be involved in training, overfitting source domains is
inevitable. As a popular regularization technique, the meta-learning training
scheme has shown its ability to resist overfitting. However, in the training
stage, current meta-learning-based methods utilize only one task along a single
optimization trajectory, which might produce a biased and noisy optimization
direction. Beyond the training stage, overfitting could also cause unstable
prediction in the test stage. In this paper, we propose a novel multi-view DG
framework to effectively reduce the overfitting in both the training and test
stage. Specifically, in the training stage, we develop a multi-view regularized
meta-learning algorithm that employs multiple optimization trajectories to
produce a suitable optimization direction for model updating. We also
theoretically show that the generalization bound could be reduced by increasing
the number of tasks in each trajectory. In the test stage, we utilize multiple
augmented images to yield a multi-view prediction to alleviate unstable
prediction, which significantly promotes model reliability. Extensive
experiments on three benchmark datasets validate that our method can find a
flat minimum to enhance generalization and outperform several state-of-the-art
approaches.
