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arXiv · 2006.14699

Learning Data Augmentation with Online Bilevel Optimization for Image Classification

Saypraseuth Mounsaveng·Issam Laradji·Ismail Ben Ayed·David Vazquez·Marco Pedersoli
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Abstract

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

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Saypraseuth Mounsaveng, Issam Laradji, Ismail Ben Ayed, David Vazquez, Marco Pedersoli. 2020-11-10. Learning Data Augmentation with Online Bilevel Optimization for Image Classification. https://arxiv.org/abs/2006.14699

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