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arXiv · 1703.10717

BEGAN: Boundary Equilibrium Generative Adversarial Networks

David Berthelot·Thomas Schumm·Luke Metz
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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.

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David Berthelot, Thomas Schumm, Luke Metz. 2017-05-31. BEGAN: Boundary Equilibrium Generative Adversarial Networks. https://arxiv.org/abs/1703.10717

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