  This paper proposes a way to understand neural network artworks as
juxtapositions of natural image cues. It is hypothesized that images with
unusual combinations of realistic visual cues are interesting, and, neural
models trained to model natural images are well-suited to creating interesting
images. Art using neural models produces new images similar to those of natural
images, but with weird and intriguing variations. This analysis is applied to
neural art based on Generative Adversarial Networks, image stylization, Deep
Dreams, and Perception Engines.
