August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
A Neural Algorithm of Artistic Style
Author Affiliations
  • Leon Gatys
    Werner Reichardt Centre for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Germany
  • Alexander Ecker
    Werner Reichardt Centre for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Germany
  • Matthias Bethge
    Werner Reichardt Centre for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Germany
Journal of Vision September 2016, Vol.16, 326. doi:10.1167/16.12.326
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Leon Gatys, Alexander Ecker, Matthias Bethge; A Neural Algorithm of Artistic Style . Journal of Vision 2016;16(12):326. doi: 10.1167/16.12.326.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

In fine art, especially painting, humans have mastered the skill to create unique visual experiences by composing a complex interplay between the content and style of an image. The algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. Recently, a class of biologically inspired vision models called Deep Neural Networks have demonstrated near-human performance in complex visual tasks such as object and face recognition. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system can separate and recombine the content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. In light of recent studies using fMRI and electrophysiology that have shown striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path towards an algorithmic understanding of how humans create and perceive artistic imagery. The algorithm introduces a novel class of stimuli that could be used to test specific computational hypotheses about the perceptual processing of artistic style.

Meeting abstract presented at VSS 2016

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×