  We are concerned with a worst-case scenario in model generalization, in the
sense that a model aims to perform well on many unseen domains while there is
only one single domain available for training. We propose Meta-Learning based
Adversarial Domain Augmentation to solve this Out-of-Domain generalization
problem. The key idea is to leverage adversarial training to create
"fictitious" yet "challenging" populations, from which a model can learn to
generalize with theoretical guarantees. To facilitate fast and desirable domain
augmentation, we cast the model training in a meta-learning scheme and use a
Wasserstein Auto-Encoder to relax the widely used worst-case constraint. We
further improve our method by integrating uncertainty quantification for
efficient domain generalization. Extensive experiments on multiple benchmark
datasets indicate its superior performance in tackling single domain
generalization.
