MMGAN: Manifold Matching Generative Adversarial Network

Noseong Park, Ankesh Anand, Joel Ruben Antony Moniz +5

It is well-known that GANs are difficult to train, and several different techniques have been proposed in order to stabilize their training. In this paper, we propose a novel train…