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 a new method named
adversarial domain augmentation to solve this Out-of-Distribution (OOD)
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 (WAE) to relax the widely used worst-case
constraint. Detailed theoretical analysis is provided to testify our
formulation, while extensive experiments on multiple benchmark datasets
indicate its superior performance in tackling single domain generalization.

本篇文章介绍了一种名为对抗域增强的方法，用于在单一训练域情况下提高模型的泛化性能，通过使用元学习方案和 WAE 来松弛最坏情况限制，并在多个基准数据集上广泛实验，验证了该方法对于缓解单一域泛化问题的优越表现。