  We study a worst-case scenario in generalization: Out-of-domain
generalization from a single source. The goal is to learn a robust model from a
single source and expect it to generalize over many unknown distributions. This
challenging problem has been seldom investigated while existing solutions
suffer from various limitations. In this paper, we propose a new solution. The
key idea is to augment the source capacity in both input and label spaces,
while the augmentation is guided by uncertainty assessment. To the best of our
knowledge, this is the first work to (1) access the generalization uncertainty
from a single source and (2) leverage it to guide both input and label
augmentation for robust generalization. The model training and deployment are
effectively organized in a Bayesian meta-learning framework. We conduct
extensive comparisons and ablation study to validate our approach. The results
prove our superior performance in a wide scope of tasks including image
classification, semantic segmentation, text classification, and speech
recognition.
