# Generative Adversarial Networks: recent developments

arXiv: https://arxiv.org/abs/1903.12266 · HTML: https://mlsanity.com/paper/1903.12266/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [ExamGAN and Twin-ExamGAN for Exam Script Generation](https://mlsanity.com/paper/2108.09656/) (2021)

## Abstract (arXiv)

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.