  In traditional generative modeling, good data representation is very often a
base for a good machine learning model. It can be linked to good
representations encoding more explanatory factors that are hidden in the
original data. With the invention of Generative Adversarial Networks (GANs), a
subclass of generative models that are able to learn representations in an
unsupervised and semi-supervised fashion, we are now able to adversarially
learn good mappings from a simple prior distribution to a target data
distribution. This paper presents an overview of recent developments in GANs
with a focus on learning latent space representations.
