Wasserstein Generative Recurrent Adversarial Networks for Image Generating

Abstract

Most generative models are generating images at a time, but in fact, painting is usually done iteratively and repeatedly. Generative Adversarial Networks (GAN) are well known for generating images, however, it is hard to train stably. To tackle this problem, we propose a framework named the Wasserstein generative recurrent adversarial networks (WGRAN), which merges Wasserstein distance with recurrent neural networks to iteratively generate realistic looking images and trains our model in an adversarial way. Therefore, our generative model is gradually generates images using the feedback of discriminate model. And our approach allows us to control the number of iterations of generation. We train our model on various image datasets and compare our model with the recurrent generative adversarial networks (GRAN) and other state-of-the-art generative models using Generative Adversarial Metric. From these experiments, we show evidence that our model has the ability to generate high quantity images.