Universal Style Transfer via Feature Transforms

Under construction!

Introduction

When viewing an image, whether it is a photograph or a painting, two types of mutually exclusive data are present. First, there is the content of the image, such as a person in a portrait. However, the content does not uniquely define the image. Consider a case where multiple artists paint a portrait of an identical subject, the results would vary despite the content being invariant. The cause of the variance is rooted in the style of each particular artist. Therefore, style transfer between two images results in the content being unaffected but just the style being copied. Typically one image is termed the content/reference image, whose style is discarded and the other image is called the style image, whose style, but not content is copied.

Deep learning techniques have been shown to be effective methods for implementing style transfer. Previous methods have been successful but with several key limitations. Either they are fast, but have very few styles that can be transferred or they can handle arbitrary styles but are no longer efficient. The presented paper establishes a compromise between these two extremes by using only whitening and colouring transforms to transfer a particular style. No training of the underlying deep network is required per style.