Data augmentation (DA) has been widely utilized to improve generalization in training deep neural networks. Recently, human-designed data augmentation has been gradually replaced by automatically learned augmentation policy. Through finding the best policy in well-designed search space of data augmentation, AutoAugment (Cubuk et al., 2019) can significantly improve validation accuracy on image classification tasks. However, this approach is not computationally practical for large-scale problems. In this paper, we develop an adversarial method to arrive at a computationally-affordable solution called Adversarial AutoAugment, which can simultaneously optimize target related object and augmentation policy search loss. The augmentation policy network attempts to increase the training loss of a target network through generating adversarial augmentation policies, while the target network can learn more robust features from harder examples to improve the generalization. In contrast to prior work, we reuse the computation in target network training for policy evaluation, and dispense with the retraining of the target network. Compared to AutoAugment, this leads to about 12x reduction in computing cost and 11x shortening in time overhead on ImageNet. We show experimental results of our approach on CIFAR-10/CIFAR-100, ImageNet, and demonstrate significant performance improvements over state-of-the-art. On CIFAR-10, we achieve a top-1 test error of 1.36%, which is the currently best performing single model. On ImageNet, we achieve a leading performance of top-1 accuracy 79.40% on ResNet-50 and 80.00% on ResNet-50-D without extra data. Massive amount of data have promoted the great success of deep learning in academia and industry. The performance of deep neural networks (DNNs) would be improved substantially when more supervised data is available or better data augmentation method is adapted. Data augmentation such as rotation, flipping, cropping, etc., is a powerful technique to increase the amount and diversity of data. Experiments show that the generalization of a neural network can be efficiently improved through manually designing data augmentation policies. However, this needs lots of knowledge of human expert, and sometimes shows the weak transferability across different tasks and datasets in practical applications. Inspired by neural architecture search (NAS) (Zoph & Le, 2016; Zoph et al., 2017; Zhong et al., 2018a; b; Guo et al., 2018) , a reinforcement learning (RL) (Williams, 1992) method called AutoAugment is proposed by Cubuk et al. (2019) , which can automatically learn the augmentation policy from data and provide an exciting performance improvement on image classification tasks. However, the computing cost is huge for training and evaluating thousands of sampled policies in the search process. Although proxy tasks, i.e., smaller models and reduced datasets, are taken to accelerate the searching process, tens of thousands of GPU-hours of consumption are still required. In addition, these data augmentation policies optimized on proxy tasks are not guaranteed to be optimal on the target task, and the fixed augmentation policy is also sub-optimal for the whole training process. In this paper, we introduce the idea of adversarial learning into automatic data augmentation. The policy network tries to combat the overfitting of the target network through generating adversarial policies with the training process. To oppose this, robust features are learned in the target network, which leads to a significant performance improvement. Meanwhile, the augmentation policy search is performed along with the training of a target network, and the computation in network training is reused for policy evaluation, which can extremely reduce the search cost and make our method more computing-efficient.
We introduce the idea of adversarial learning into automatic data augmentation to improve the generalization  of a targe network. A technique called Adversarial AutoAugment which dynamically learns good data augmentation policies during training using an adversarial approach.