Domain Generalization by Rejecting Extreme Augmentations
Masih Aminbeidokhti Fidel A. Guerrero Peña Heitor Rapela Medeiros Thomas Dubail Eric Granger Marco Pedersoli
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}