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Aesthetics and neural network image representations

2021-09-16 16:50:22
Romuald A. Janik
arXiv_CV
     
Abstract

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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URL

https://arxiv.org/abs/2109.08103

PDF

https://arxiv.org/pdf/2109.08103.pdf

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