  We analyze the spaces of images encoded by generative neural networks of the
BigGAN architecture. We find that generic multiplicative perturbations of
neural network parameters away from the photo-realistic point often lead to
networks generating images which appear as "artistic renditions" of the
corresponding objects. This demonstrates an emergence of aesthetic properties
directly from the structure of the photo-realistic visual environment as
encoded in its neural network parametrization. Moreover, modifying a deep
semantic part of the neural network leads to the appearance of symbolic visual
representations. None of the considered networks had any access to images of
human-made art.
