d423: BEGAN: Generative Adversarial Networks Faces Generation

BEGAN: Boundary Equilibrium Generative Adversarial Networks – Faces Generation:

BEGAN - Generative Adversarial Networks Faces Generation

Discussion: https://www.reddit.com/r/MachineLearning/comments/64rayf/r_began_state_of_the_art_generation_of_faces_with/

Paper: https://arxiv.org/abs/1703.10717 | PDF | Discussion: https://www.reddit.com/r/MachineLearning/comments/633jal/r170310717_began_boundary_equilibrium_generative/

Abstract: 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. We focus on the image generation task, setting a new milestone in visual quality, even at higher resolutions. This is achieved while using a relatively simple model architecture and a standard training procedure.