Domain Generalization by Rejecting Extreme Augmentations
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866911197893230592 |
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| author | Aminbeidokhti, Masih Peña, Fidel A. Guerrero Medeiros, Heitor Rapela Dubail, Thomas Granger, Eric Pedersoli, Marco |
| author_facet | Aminbeidokhti, Masih Peña, Fidel A. Guerrero Medeiros, Heitor Rapela Dubail, Thomas Granger, Eric Pedersoli, Marco |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_06670 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Domain Generalization by Rejecting Extreme Augmentations Aminbeidokhti, Masih Peña, Fidel A. Guerrero Medeiros, Heitor Rapela Dubail, Thomas Granger, Eric Pedersoli, Marco Machine Learning Computer Vision and Pattern Recognition 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 |
| title | Domain Generalization by Rejecting Extreme Augmentations |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.06670 |