  Generative Adversarial Networks (GANs) have been used to model the underlying
probability distribution of sample based datasets. GANs are notoriuos for
training difficulties and their dependence on arbitrary hyperparameters. One
recent improvement in GAN literature is to use the Wasserstein distance as loss
function leading to Wasserstein Generative Adversarial Networks (WGANs). Using
this as a basis, we show various ways in which the Wasserstein distance is
estimated for the task of generative modelling. Additionally, the secrets in
training such models are shown and summarized at the end of this work. Where
applicable, we extend current works to different algorithms, different cost
functions, and different regularization schemes to improve generative models.
