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

Understand

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.