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Improving Deep Interactive Evolution with a Style-Based Generator for Artistic Expression and Creative Exploration.

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This summary is machine-generated.

StyleIE, a new method for deep interactive evolution (DeepIE), offers improved control in art generation by using a StyleGAN model. StyleIE performs equally to DeepIE in open-ended tasks but excels in constrained ones.

Keywords:
StyleGANdeep interactive evolutionevolutionary artgenerative adversarial networksinteractive evolutionary computationlatent space explorationneural art

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Deep interactive evolution (DeepIE) integrates user preferences with generative adversarial networks (GANs) for controlled output.
  • Traditional GAN latent spaces suffer from feature entanglement, hindering DeepIE's practical applications.

Purpose of the Study:

  • To propose StyleIE, a novel DeepIE variation utilizing a StyleGAN's disentangled latent space.
  • To compare the performance of StyleIE against traditional DeepIE in art generation tasks.

Main Methods:

  • Implemented DeepIE using a StyleGAN generator trained on the WikiArt dataset.
  • Conducted two AB/BA crossover user tests comparing DeepIE and StyleIE.
  • Collected self-rated performance evaluations via questionnaires.

Main Results:

  • StyleIE and DeepIE showed comparable performance in open-ended art generation tasks with relaxed constraints.
  • StyleIE demonstrated superior performance in close-ended and more constrained art generation tasks.

Conclusions:

  • StyleIE effectively leverages StyleGAN's disentangled latent space for enhanced user control in DeepIE.
  • The choice between StyleIE and DeepIE depends on task constraints, with StyleIE being advantageous for more defined goals.