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Joint optimize data augmentation and network training

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by yawei luo

Published in: Technology
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Joint optimize data augmentation and network training

  1. 1. Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation
  2. 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. 3. Networks
  4. 4. Training objective
  5. 5. Adversarial scaling and rotating
  6. 6. Adversarial Hierarchical Occluding
  7. 7. Algorithm
  8. 8. Reward & Penalty
  9. 9. Result
  10. 10. Result

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