# Exploring Latent Dimensions of Crowd-sourced Creativity

arXiv: https://arxiv.org/abs/2112.06978 · HTML: https://mlsanity.com/paper/2112.06978/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [DeepFakes: a New Threat to Face Recognition? Assessment and Detection](https://mlsanity.com/paper/1812.08685/) (2018)
- [GANalyze: Toward Visual Definitions of Cognitive Image Properties](https://mlsanity.com/paper/1906.10112/) (2019)
- [Toward Quantifying Ambiguities in Artistic Images](https://mlsanity.com/paper/2008.09688/) (2020)
- [The power of pictures: using ML assisted image generation to engage the crowd in complex socioscientific problems](https://mlsanity.com/paper/2010.12324/) (2020)
- [Graph2Pix: A Graph-Based Image to Image Translation Framework](https://mlsanity.com/paper/2108.09752/) (2021)

## Abstract (arXiv)

Recently, the discovery of interpretable directions in the latent spaces of pre-trained GANs has become a popular topic. While existing works mostly consider directions for semantic image manipulations, we focus on an abstract property: creativity. Can we manipulate an image to be more or less creative? We build our work on the largest AI-based creativity platform, Artbreeder, where users can generate images using pre-trained GAN models. We explore the latent dimensions of images generated on this platform and present a novel framework for manipulating images to make them more creative. Our code and dataset are available at http://github.com/catlab-team/latentcreative.