A Neural Algorithm of Artistic Style

TL;DR

This paper introduces a neural network-based algorithm capable of creating high-quality artistic images by separating and recombining content and style, offering insights into both artificial and human artistic perception.

Contribution

It presents a novel neural algorithm that synthesizes artistic images by manipulating content and style representations within a deep neural network.

Findings

The system produces perceptually high-quality artistic images.

Neural representations effectively separate content and style.

The approach offers a potential model for understanding human artistic creation.

Abstract

In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.