Image Generation Using Different Models Of Generative Adversarial Network

Abstract

Generative adversarial networks (GANs) can be used in modeling highly complex distributions for real world data, especially images. This paper compares between two different models of the Generative Adversarial Networks: the Multi-Agent Diverse Generative Adversarial Networks (MAD-GAN) which consists of multi-generator and one discriminator and the Generative Multi-Adversarial Networks (GMAN) that has multiple discriminators and one generator. The results show that both MAD-GAN and GMAN outperformed the DCGAN. In addition, MAD-GAN performs better than GMAN when avoiding mode collapse or when the dataset contains many different modes.