A survey on generative adversarial networks and their variants methods

Data science becomes creative with generative adversarial networks (GANs) which have had a big success since they were introduced in 2014 by Ian J. Goodfellow and co-authors. In technical term the GANs are based on the unsupervised learning of two artificial neural networks called Generator and Discriminator both trained under the adversarial learning idea. The major goal of GAN is to generate new samples that estimate the potential distribution of real data. Due to its huge success, many modified versions have been proposed in the last two years. We summarize in this review paper GAN's background, architecture and its application fields. Then, we discuss the different extensions of GAN over the original model and provide a comparative analysis of these techniques.