Non-Parametric Style Transfer

TL;DR

This paper introduces a non-parametric, distribution-based approach to style transfer that improves the similarity of stylized images to target styles by matching feature map distributions more precisely.

Contribution

It proposes a new feature transform layer for exact distribution matching and analyzes style losses to enhance style transfer quality.

Findings

Stylized images are more similar to target styles across multiple measures.

The method preserves content clarity while improving style fidelity.

Experimental results validate the effectiveness of the distribution matching approach.

Abstract

Recent feed-forward neural methods of arbitrary image style transfer mainly utilized encoded feature map upto its second-order statistics, i.e., linearly transformed the encoded feature map of a content image to have the same mean and variance (or covariance) of a target style feature map. In this work, we extend the second-order statistical feature matching into a general distribution matching based on the understanding that style of an image is represented by the distribution of responses from receptive fields. For this generalization, first, we propose a new feature transform layer that exactly matches the feature map distribution of content image into that of target style image. Second, we analyze the recent style losses consistent with our new feature transform layer to train a decoder network which generates a style transferred image from the transformed feature map. Based on our experimental results, it is proven that the stylized images obtained with our method are more similar with the target style images in all existing style measures without losing content clearness.