CAN: Creative Adversarial Networks, Generating "Art" by Learning About
  Styles and Deviating from Style Norms
  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. Human subjects even rated the
generated images higher on various scales.
