  This paper explores visual indeterminacy as a description for artwork created
with Generative Adversarial Networks (GANs). Visual indeterminacy describes
images which 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. It is hypothesized that
indeterminacy is a consequence of a powerful-but-imperfect image synthesis
model that must combine general classes of objects, scenes, and textures.
