Domain Generalization by Rejecting Extreme Augmentations

• URL: http://arxiv.org/abs/2310.06670v1
• Date: Tue, 10 Oct 2023 14:46:22 GMT
• Title: Domain Generalization by Rejecting Extreme Augmentations
• Authors: Masih Aminbeidokhti, Fidel A. Guerrero Pe\~na, Heitor Rapela Medeiros, Thomas Dubail, Eric Granger, Marco Pedersoli
• Abstract summary: We show that for out-of-domain and domain generalization settings, data augmentation can provide a conspicuous and robust improvement in performance. 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.
• Score: 13.114457707388283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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}

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