# Dropout Induced Noise for Co-Creative GAN Systems

arXiv: https://arxiv.org/abs/1909.04474 · HTML: https://mlsanity.com/paper/1909.04474/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

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

- [Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion](https://mlsanity.com/paper/2305.03098/) (2023)

## Abstract (arXiv)

This paper demonstrates how Dropout can be used in Generative Adversarial Networks to generate multiple different outputs to one input. This method is thought as an alternative to latent space exploration, especially if constraints in the input should be preserved, like in A-to-B translation tasks.