Conditional generative recurrent adversarial networks

Abstract

Generative adversarial networks (GANs) introduced by Goodfellow et al. since their advent have had a number of improvements and applications in image generation tasks and unsupervised learning. Recurrent model and the conditional models are two derivations of GANs. In this paper, conditional recurrent GAN is proposed. By using conditional settings in recurrent GANs, they can be used to generate state-of-the-art images. The conditional and recurrent models are compared with the proposed model using the generative adversarial metric proposed by Im et al. where the discriminator of one model competes against the generator of another. The results show that the proposed model outperforms the other two models.