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Improved Human Pose Estimation through Adversarial Data Augmentation

IHPEADA

Deep models are usually trained in a two-phase paradigm, where data collection and network training are separated. This may not be efficient since data collection is blind to network training. Why not jointly optimize these two? We propose adversarial data augmentation. The key idea is to develop a generator ({\it e.g.} an augmentation network) that competes against a discriminator ({\it e.g.} a target network) by generating hard examples online. The generator explores weaknesses of the discriminator, while the discriminator learns from hard augmentations to get better performance. Moreover, a reward/penalty strategy is designed to guarantee the joint training and avoid problematic convergence behaviors. We investigate human pose estimation to validate our idea. Extensive ablation studies, as well as comparisons on benchmarks, prove that our method can significantly improve the state of the art without additional data effort.

 

Publications

  • : Improved Human Pose Estimation through Adversarial Data Augmentation, The Internatioanl Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

 

 

 

 

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