Cubuk, Ekin D., Zoph, Barret, Mane, Dandelion, Vasudevan, Vijay, Le, Quoc V.
In this paper, we take a closer look at data augmentation for images, and
describe a simple procedure called AutoAugment to search for improved data
augmentation policies. Our key insight is to create a search space of data
augmentation policies, evaluating the quality of a particular policy directly
on the dataset of interest. 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 83.54%. On CIFAR-10, we achieve an error rate of 1.48%,
which is 0.65% better than the previous state-of-the-art. On reduced data
settings, AutoAugment performs comparably to semi-supervised methods without
using any unlabeled examples. Finally, policies learned from one dataset can be
transferred to work well on other similar datasets. For example, the policy
learned on ImageNet allows us to achieve state-of-the-art accuracy on the fine
grained visual classification dataset Stanford Cars, without fine-tuning
weights pre-trained on additional data.
Cubuk, Ekin D., Zoph, Barret, Mane, Dandelion, Vasudevan, Vijay, Le, Quoc V.
In this paper, we take a closer look at data augmentation for images, and describe a simple procedure called AutoAugment to search for improved data augmentation policies. Our key insight is to create a search space of data augmentation policies, evaluating the quality of a particular policy directly on the dataset of interest. 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 83.54%. On CIFAR-10, we achieve an error rate of 1.48%, which is 0.65% better than the previous state-of-the-art. On reduced data settings, AutoAugment performs comparably to semi-supervised methods without using any unlabeled examples. Finally, policies learned from one dataset can be transferred to work well on other similar datasets. For example, the policy learned on ImageNet allows us to achieve state-of-the-art accuracy on the fine grained visual classification dataset Stanford Cars, without fine-tuning weights pre-trained on additional data.
https://arxiv.org/abs/1805.09501