Aesthetics and neural network image representations


Abstract in English

We analyze the spaces of images encoded by generative networks of the BigGAN architecture. We find that generic multiplicative perturbations away from the photo-realistic point often lead to 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 environment coupled with its neural network parametrization. Moreover, modifying a deep semantic part of the neural network encoding leads to the appearance of symbolic visual representations.

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