Data augmentation is one of the most effective techniques for regularizing
deep learning models and improving their recognition performance in a variety
of tasks and domains. However, this holds for standard in-domain settings, in
which the training and test data follow the same distribution. For the
out-of-domain case, where the test data follow a different and unknown
distribution, the best recipe for data augmentation is unclear. In this paper,
we show that for out-of-domain and domain generalization settings, data
augmentation can provide a conspicuous and robust improvement in performance.
To do that, we propose a simple training procedure: (i) use uniform sampling on
standard data augmentation transformations; (ii) increase the strength
transformations to account for the higher data variance expected when working
out-of-domain, and (iii) devise a new reward function to reject extreme
transformations that can harm the training. With this procedure, our data
augmentation scheme achieves a level of accuracy that is comparable to or
better than state-of-the-art methods on benchmark domain generalization
datasets. Code: \url{https://github.com/Masseeh/DCAug