Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation

Understand

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.