gans-eval

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