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Data augmentation is a key practice in machine learning for improvinggeneralization performance. However, finding the best data augmentationhyperparameters requires domain knowledge or a computationally demandingsearch. We address this issue by proposing an efficient approach toautomatically train a network that learns an effective distribution oftransformations to improve its generalization. Using bilevel optimization, wedirectly optimize the data augmentation parameters using a validation set. Thisframework can be used as a general solution to learn the optimal dataaugmentation jointly with an end task model like a classifier. Results showthat our joint training method produces an image classification accuracy thatis comparable to or better than carefully hand-crafted data augmentation. Yet,it does not need an expensive external validation loop on the data augmentationhyperparameters.

Learning Data Augmentation with Online Bilevel Optimization for Image Classification