# Generative Adversarial Networks: recent developments

## TL;DR

This paper reviews recent advances in Generative Adversarial Networks (GANs), emphasizing their ability to learn meaningful latent space representations in unsupervised and semi-supervised settings.

## Contribution

It provides a comprehensive overview of recent developments in GANs, highlighting progress in learning latent space representations.

## Key findings

- Enhanced understanding of GANs' ability to learn data representations
- Recent techniques improve the quality of generated data
- Progress in semi-supervised learning with GANs

## Abstract

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.