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.

翻译：我们分析了由BigGAN架构生成式神经网络编码的图像空间。研究发现，远离逼真图像点的神经网络参数通用乘性扰动，常会导致网络生成"艺术化再现"对应物体的图像。这证明了审美属性直接从神经网络参数化所编码的逼真视觉环境结构中涌现。此外，修改神经网络的深层语义部分会引发象征性视觉表征的出现。所考察的神经网络均未接触过任何人造艺术图像。