# Modeling documents with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1612.09122 · HTML: https://mlsanity.com/paper/1612.09122/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2016.

## Built on by (most cited first, 3 of 3)

- [Divergent Creativity in Humans and Large Language Models](https://mlsanity.com/paper/2405.13012/) (2024)
- [Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs](https://mlsanity.com/paper/2106.03393/) (2021)
- [Adversarial Conversational Shaping for Intelligent Agents](https://mlsanity.com/paper/2307.11785/) (2023)

## Abstract (arXiv)

This paper describes a method for using Generative Adversarial Networks to learn distributed representations of natural language documents. We propose a model that is based on the recently proposed Energy-Based GAN, but instead uses a Denoising Autoencoder as the discriminator network. Document representations are extracted from the hidden layer of the discriminator and evaluated both quantitatively and qualitatively.