Fisher GAN

@inproceedings{Mroueh2017FisherG,
  title={Fisher GAN},
  author={Youssef Mroueh and Tom Sercu},
  booktitle={NIPS},
  year={2017}
}
Generative Adversarial Networks (GANs) are powerful models for learning complex distributions. Stable training of GANs has been addressed in many recent works which explore different metrics between distributions. In this paper we introduce Fisher GAN which fits within the Integral Probability Metrics (IPM) framework for training GANs. Fisher GAN defines a critic with a data dependent constraint on its second order moments. We show in this paper that Fisher GAN allows for stable and time… CONTINUE READING

Similar Papers

Loading similar papers…