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arXiv · 1905.07373

Online Hyper-parameter Learning for Auto-Augmentation Strategy

Chen Lin·Minghao Guo·Chuming Li·Yuan Xin·Wei Wu·Dahua Lin·Wanli Ouyang·Junjie Yan
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Abstract

Data augmentation is critical to the success of modern deep learning techniques. In this paper, we propose Online Hyper-parameter Learning for Auto-Augmentation (OHL-Auto-Aug), an economical solution that learns the augmentation policy distribution along with network training. Unlike previous methods on auto-augmentation that search augmentation strategies in an offline manner, our method formulates the augmentation policy as a parameterized probability distribution, thus allowing its parameters to be optimized jointly with network parameters. Our proposed OHL-Auto-Aug eliminates the need of re-training and dramatically reduces the cost of the overall search process, while establishes significantly accuracy improvements over baseline models. On both CIFAR-10 and ImageNet, our method achieves remarkable on search accuracy, 60x faster on CIFAR-10 and 24x faster on ImageNet, while maintaining competitive accuracies.

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Chen Lin, Minghao Guo, Chuming Li, Yuan Xin, Wei Wu, Dahua Lin, Wanli Ouyang, Junjie Yan. 2019-05-17. Online Hyper-parameter Learning for Auto-Augmentation Strategy. https://arxiv.org/abs/1905.07373

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