A Neural Algorithm of Artistic Style

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.1, 2 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,3–7 our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.

Comparing Artificial Artists

Last Wednesday, “A Neural Algorithm of Artistic Style” was posted to ArXiv, featuring some of the most compelling imagery generated by deep convolutional neural networks since Google Research’s “DeepDream” post.

On Sunday, Kai Sheng Tai posted the first public implementation. I immediately stopped working on my implementation and started playing with his. Unfortunately, his results don’t quite match the paper, and it’s unclear why. I’m just getting started with this topic, so as I learn I want to share my understanding of the algorithm here, along with some results I got from testing his code.