This post contains all you need to learn Generative Adversial Networks, including video, tutorials and related papers.
the original GAN paper in 2014: pdf
before you continue to read the WGAN, I recommend to read this excellent blog first.
the author firstly analysis the defects of GANs and give solution mathematically: Towards-Principled-Methods-for-Training-Generative-Adversarial-Networks
then the original WGAN, which is mostly used now: Wasserstein-GAN
another influential works is DCGAN, which leverages the Generative Model and Discriminator at the same time.
hopefully, this list will help you become very clear with GAN and its variants.