  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 Aircraft,
and Stanford Cars.
