  Domain shift refers to the well known problem that a model trained in one
source domain performs poorly when applied to a target domain with different
statistics. {Domain Generalization} (DG) techniques attempt to alleviate this
issue by producing models which by design generalize well to novel testing
domains. We propose a novel {meta-learning} method for domain generalization.
Rather than designing a specific model that is robust to domain shift as in
most previous DG work, we propose a model agnostic training procedure for DG.
Our algorithm simulates train/test domain shift during training by synthesizing
virtual testing domains within each mini-batch. The meta-optimization objective
requires that steps to improve training domain performance should also improve
testing domain performance. This meta-learning procedure trains models with
good generalization ability to novel domains. We evaluate our method and
achieve state of the art results on a recent cross-domain image classification
benchmark, as well demonstrating its potential on two classic reinforcement
learning tasks.
