Aesthetics and neural network image representations.
Sci Rep
; 13(1): 11428, 2023 Jul 15.
Article
em En
| MEDLINE
| ID: mdl-37454170
ABSTRACT
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.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2023
Tipo de documento:
Article