  Can we generate abstract aesthetic images without bias from natural or human
selected image corpi? Are aesthetic images singled out in their correlation
functions? In this paper we give answers to these and more questions. We
generate images using compositional pattern-producing networks with random
weights and varying architecture. We demonstrate that even with the randomly
selected weights the correlation functions remain largely determined by the
network architecture. In a controlled experiment, human subjects picked
aesthetic images out of a large dataset of all generated images. Statistical
analysis reveals that the correlation function is indeed different for
aesthetic images.
