by yawei luo

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

2. Motivation
• The wildly used random data augmentations
are suboptimal. They are usually “blindly”
sampled from static distributions without
considering the training status of the target
network. This may produce many ineffective
samples that are either too hard or too easy
for the target network to learn from.

3. Networks

4. Training objective

5. Adversarial scaling and rotating

6. Adversarial Hierarchical Occluding

9. Algorithm

10. Reward & Penalty

11. Result

12. Result