  Machine learning models are intrinsically vulnerable to domain shift between
training and testing data, resulting in poor performance in novel domains.
Domain generalization (DG) aims to overcome the problem by leveraging multiple
source domains to learn a domain-generalizable model. In this paper, we propose
a novel augmentation-based DG approach, dubbed AugLearn. Different from
existing data augmentation methods, our AugLearn views a data augmentation
module as hyper-parameters of a classification model and optimizes the module
together with the model via meta-learning. Specifically, at each training step,
AugLearn (i) divides source domains into a pseudo source and a pseudo target
set, and (ii) trains the augmentation module in such a way that the augmented
(synthetic) images can make the model generalize well on the pseudo target set.
Moreover, to overcome the expensive second-order gradient computation during
meta-learning, we formulate an efficient joint training algorithm, for both the
augmentation module and the classification model, based on the implicit
function theorem. With the flexibility of augmenting data in both time and
frequency spaces, AugLearn shows effectiveness on three standard DG benchmarks,
PACS, Office-Home and Digits-DG.
