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Jointly Optimize Data Augmentation
and Network Training: Adversarial
Data Augmentation in Human Pose
Estimation
Motivation
• The wildly used random data augmentations
are suboptimal. They are usually “blindly”
sampled from static dist...
Networks
Training objective
Adversarial scaling and rotating
Adversarial Hierarchical Occluding
Algorithm
Reward & Penalty
Result
Result
Joint optimize data augmentation and network training
Joint optimize data augmentation and network training
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Joint optimize data augmentation and network training

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by yawei luo

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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

by yawei luo

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