Exploring Latent Dimensions of Crowd-sourced Creativity
Abstract
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.
Cite
@article{arxiv.2112.06978,
title = {Exploring Latent Dimensions of Crowd-sourced Creativity},
author = {Umut Kocasari and Alperen Bag and Efehan Atici and Pinar Yanardag},
journal= {arXiv preprint arXiv:2112.06978},
year = {2021}
}
Comments
5th Workshop on Machine Learning for Creativity and Design (NeurIPS 2021), Sydney, Australia