Data augmentation methods are indispensable heuristics to boost the performance of deep neural networks, especially in image recognition tasks. Recently, several studies have shown that augmentation strategies found by search algorithms outperform hand-made strategies. Such methods employ black-box search algorithms over image transformations with continuous or discrete parameters and require a long time to obtain better strategies. In this paper, we propose a differentiable policy search pipeline for data augmentation, which is much faster than previous methods. We introduce approximate gradients for several transformation operations with discrete parameters as well as the differentiable mechanism for selecting operations. As the objective of training, we minimize the distance between the distributions of augmented data and the original data, which can be differentiated. We show that our method, Faster AutoAugment, achieves significantly faster searching than prior work without a performance drop.

本文提出了一种可微分的策略搜索方法，用于数据增强，该方法比之前的方法更快，使用的是离散参数变换的近似梯度和可分辨操作选择机制。我们的方法通过最小化增强数据与原始数据之间的分布距离来优化，比之前的工作实现了显著更快的搜索，而不会出现性能下降。

使用反向传播学习增强策略的更快自动增强