AutoAugment: Learning Augmentation Strategies from Data

Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub- policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5% which is 0.4% better than the previous record of 83.1%. On CIFAR-10, we achieve an error rate of 1.5%, which is 0.6% better than the previous state-of-the-art. Augmentation policies we find are transferable between datasets. The policy learned on ImageNet transfers well to achieve significant improvements on other datasets, such as Oxford Flowers, Caltech-101, Oxford-IIT Pets, FGVC Air- craft, and Stanford Cars.

https://arxiv.org/pdf/1805.09501.pdf

数据扩增作为一个有效的手段一直在机器学习领域受到重视,目前现有的数据扩增手段均为手动设计。本文提出一种自动化的数据扩增方法,核心概念是在一个决策空间中自动搜索子决策,这些子决策由两个简单的数据扩增方法构成。模型架构由一个控制器(RNN)构成,RNN通过获取子网络的验证精度调整自己的参数,从而选择更加优化的子策略。实验证明,本方法可以有效地提高传统图像识别模型在各个数据集上的表现。

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