MMGAN: Generative Adversarial Networks for Multi-Modal Distributions

Understand

Over the past years, Generative Adversarial Networks (GANs) have shown a remarkable generation performance especially in image synthesis.

• Unfortunately, they are also known for having an unstable training process and might loose parts of the data distribution for heterogeneous input data.
• In this paper, we propose a novel GAN extension for multi-modal distribution learning (MMGAN).
• In our approach, we model the latent space as a Gaussian mixture model with a number of clusters referring to the number of disconnected data manifolds in the observation space, and include a clustering network, which relates each data manifold to one Gaussian cluster.