How to Train Your DRAGAN

@article{Kodali2017HowTT,
  title={How to Train Your DRAGAN},
  author={Naveen Kodali and Jacob D. Abernethy and James Hays and Zsolt Kira},
  journal={CoRR},
  year={2017},
  volume={abs/1705.07215}
}
Generative Adversarial Networks have emerged as an effective technique for estimating data distributions. The basic setup consists of two deep networks playing against each other in a zero-sum game setting. However, it is not understood if the networks reach an equilibrium eventually and what dynamics makes this possible. The current GAN training procedure, which involves simultaneous gradient descent, lacks a clear game-theoretic justification in the literature. In this paper, we introduce… CONTINUE READING
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