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We propose a new system for generating art. The system generates art bylooking at art and learning about style; and becomes creative by increasing thearousal potential of the generated art by deviating from the learned styles. Webuild over Generative Adversarial Networks (GAN), which have shown the abilityto learn to generate novel images simulating a given distribution. We arguethat such networks are limited in their ability to generate creative productsin their original design. We propose modifications to its objective to make itcapable of generating creative art by maximizing deviation from establishedstyles and minimizing deviation from art distribution. We conducted experimentsto compare the response of human subjects to the generated art with theirresponse to art created by artists. The results show that human subjects couldnot distinguish art generated by the proposed system from art generated bycontemporary artists and shown in top art fairs. Human subjects even rated thegenerated images higher on various scales.

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