# Quantitative Evaluation of Style Transfer

arXiv: https://arxiv.org/abs/1804.00118 · HTML: https://mlsanity.com/paper/1804.00118/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate](https://mlsanity.com/paper/2304.09728/) (2023)
- [Depth-aware Neural Style Transfer using Instance Normalization](https://mlsanity.com/paper/2203.09242/) (2022)
- [Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metric](https://mlsanity.com/paper/2208.00623/) (2022)
- [Non-Parametric Neural Style Transfer](https://mlsanity.com/paper/2108.12847/) (2021)

## Abstract (arXiv)

Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image. There is a rich literature of variant methods. However, evaluation procedures are qualitative, mostly involving user studies. We describe a novel quantitative evaluation procedure. One plots effectiveness (a measure of the extent to which the style was transferred) against coherence (a measure of the extent to which the transferred image decomposes into objects in the same way that the content image does) to obtain an EC plot. We construct EC plots comparing a number of recent style transfer methods. Most methods control within-layer gram matrices, but we also investigate a method that controls cross-layer gram matrices. These EC plots reveal a number of intriguing properties of recent style transfer methods. The style used has a strong effect on the outcome, for all methods. Using large style weights does not necessarily improve effectiveness, and can produce worse results. Cross-layer gram matrices easily beat all other methods, but some styles remain difficult for all methods. Ensemble methods show real promise. It is likely that, for current methods, each style requires a different choice of weights to obtain the best results, so that automated weight setting methods are desirable. Finally, we show evidence comparing our EC evaluations to human evaluations.