CAN: Creative Adversarial Networks Generating “Art” by Learning About Styles and Deviating from Style Norms


Publication/Creation Date
June 21 2017
Creators/Contributors
Ahmed Elgammal (creator)
Bingchen Liu (creator)
Mohamed Elhoseiny (creator)
Marian Mazzone (creator)
Rutgers University (contributor)
College Of Charleston (contributor)
Cornell University (contributor)
The Art And Artificial Intelligence Laboratory (contributor)
Media Type
Corporate Paper
Persuasive Intent
Academic
Description
Abstract:

We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution. We argue that such networks are limited in their ability to generate creative products in their original design. We propose modifications to its objective to make it capable of generating creative art by maximizing deviation from established styles and minimizing deviation from art distribution. We conducted experiments to compare the response of human subjects to the generated art with their response to art created by artists. The results show that human subjects could not distinguish art generated by the proposed system from art generated by contemporary artists and shown in top art fairs. 
HCI Platform
Other
Discursive Type
Inventions
Location on Body
Not On The Body
Source
https://arxiv.org/abs/1706.07068
https://sites.google.com/site/digihumanlab/home

Date archived
November 16 2017
Last edited
August 1 2020
How to cite this entry
Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, Marian Mazzone. (June 21 2017). "CAN: Creative Adversarial Networks Generating “Art” by Learning About Styles and Deviating from Style Norms". International Conference on Computational Creativity (ICCC). Fabric of Digital Life. https://fabricofdigitallife.com/Detail/objects/2610