Implementation of different models for Generative Adversarial Networks as decribed in:
- Generative Adversarial Networks
- Wasserstein GAN
- Improved Training of Wasserstein GANs
- The Cramer Distance as a Solution to Biased Wasserstein Gradients
Also consensus optimization from: The Numerics of GANs