CAN: Creative adversarial networks generating “Art” by learning about styles and deviating from style norms

Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, Marian Mazzone

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

197 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 8th International Conference on Computational Creativity, ICCC 2017
EditorsAshok Goel, Anna Jordanous, Alison Pease
PublisherGeorgia Institute of Technology
ISBN (Electronic)9780692895641
StatePublished - 2017
Event8th International Conference on Computational Creativity, ICCC 2017 - Atlanta, United States
Duration: Jun 19 2017Jun 23 2017

Publication series

NameProceedings of the 8th International Conference on Computational Creativity, ICCC 2017

Conference

Conference8th International Conference on Computational Creativity, ICCC 2017
Country/TerritoryUnited States
CityAtlanta
Period06/19/1706/23/17

Bibliographical note

Publisher Copyright:
© ICCC 2017.

ASJC Scopus subject areas

  • Computational Theory and Mathematics

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