  Generalization capability to unseen domains is crucial for machine learning
models when deploying to real-world conditions. We investigate the challenging
problem of domain generalization, i.e., training a model on multi-domain source
data such that it can directly generalize to target domains with unknown
statistics. We adopt a model-agnostic learning paradigm with gradient-based
meta-train and meta-test procedures to expose the optimization to domain shift.
Further, we introduce two complementary losses which explicitly regularize the
semantic structure of the feature space. Globally, we align a derived soft
confusion matrix to preserve general knowledge about inter-class relationships.
Locally, we promote domain-independent class-specific cohesion and separation
of sample features with a metric-learning component. The effectiveness of our
method is demonstrated with new state-of-the-art results on two common object
recognition benchmarks. Our method also shows consistent improvement on a
medical image segmentation task.
