  Random data augmentation is a critical technique to avoid overfitting in
training deep neural network models. However, data augmentation and network
training are usually treated as two isolated processes, limiting the
effectiveness of network training. Why not jointly optimize the two? We propose
adversarial data augmentation to address this limitation. The main idea is to
design an augmentation network (generator) that competes against a target
network (discriminator) by generating `hard' augmentation operations online.
The augmentation network explores the weaknesses of the target network, while
the latter learns from `hard' augmentations to achieve better performance. We
also design a reward/penalty strategy for effective joint training. We
demonstrate our approach on the problem of human pose estimation and carry out
a comprehensive experimental analysis, showing that our method can
significantly improve state-of-the-art models without additional data efforts.
