Comprehensive Guide to Generative Adversarial Networks and Wasserstein GANs

The year 2017 was a period of scientific breakthroughs in deep learning, with the publication of numerous research papers. Every year seems like a big leap toward artificial general intelligence, or AGI. One exciting development involves generative modelling and the use of Wasserstein GANs (Generative Adversarial Networks). An influential paper on the topic has completely changed the approach to generative modelling, moving beyond the time when Ian Goodfellow published the original GAN paper. This paper differs from earlier work: the training algorithm is backed up by theory, and few examples exist where theory-justified papers gave good empirical results.