BEGAN: Boundary Equilibrium Generative Adversarial Networks

Understand

We propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder based Generative Adversarial Networks.

• This method balances the generator and discriminator during training.
• Additionally, it provides a new approximate convergence measure, fast and stable training and high visual quality.
• We also derive a way of controlling the trade-off between image diversity and visual quality.