Visual indeterminacy in GAN art

Abstract

This paper explores visual indeterminacy as a description for artwork created with Generative Adversarial Networks (GANs). Visual indeterminacy describes images that appear to depict real scenes, but on closer examination, defy coherent spatial interpretation. GAN models seem to be predisposed to producing indeterminate images, and indeterminacy is a key feature of much modern representational art, as well as most GAN art. The author hypothesizes that indeterminacy is a consequence of a powerful-but-imperfect image synthesis model that must combine general classes of objects, scenes and textures.