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Aesthetics and neural network image representations.

Romuald A Janik1

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Generative neural networks like BigGAN can produce artistic images by altering parameters, revealing emergent aesthetic properties. Deep semantic changes create symbolic visuals without prior art exposure.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Generative Models

Background:

  • Generative neural networks, such as BigGAN, encode complex visual environments.
  • Understanding the latent space of these models is crucial for controlling image generation.

Purpose of the Study:

  • To investigate the impact of parameter perturbations on image generation in BigGAN.
  • To explore the emergence of aesthetic and symbolic representations in generated images.

Main Methods:

  • Analysis of image spaces generated by BigGAN.
  • Application of generic multiplicative perturbations to neural network parameters.
  • Modification of deep semantic components within the network architecture.

Main Results:

  • Perturbing BigGAN parameters away from photorealism results in images with artistic qualities.
  • Modifying deep semantic network parts leads to symbolic visual outputs.
  • These aesthetic and symbolic properties emerge intrinsically, without exposure to human art.

Conclusions:

  • The structure of the photorealistic visual environment, as encoded in neural network parameters, inherently supports aesthetic and symbolic renditions.
  • Generative models possess latent capabilities for artistic and symbolic expression.
  • Further research into latent space manipulation can unlock novel creative applications for AI.