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.

本文介绍了使用Online Hyper-parameter Learning for Auto-Augmentation解决基于离线搜索增广策略所存在的高昂成本问题。在CIFAR-10和ImageNet数据集上，我们的方法取得了显著的准确率提高，并且速度分别比现有方法快了60倍和24倍，同时保持了有竞争力的准确性。

在线超参数学习用于自动增强策略