Domain Generalization by Rejecting Extreme Augmentations
This addresses domain generalization for machine learning models, improving robustness to unseen data distributions, though it appears incremental as it builds on standard data augmentation techniques.
The paper tackles the problem of data augmentation in out-of-domain and domain generalization settings, where test data follow a different distribution, by proposing a training procedure that uses uniform sampling, increases transformation strength, and rejects extreme augmentations, achieving accuracy comparable to or better than state-of-the-art methods on benchmark datasets.
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: https://github.com/Masseeh/DCAug