# Arbitrary Style Transfer with Style-Attentional Networks

## TL;DR

This paper introduces SANet, a style-attentional network for arbitrary style transfer that effectively balances content preservation and style pattern integration, producing high-quality, real-time stylized images.

## Contribution

The paper presents a novel style-attentional network with identity loss and multi-level features for improved style transfer quality and efficiency.

## Key findings

- Produces higher quality stylized images than state-of-the-art methods
- Operates in real-time with efficient processing
- Effectively balances content and style patterns

## Abstract

Arbitrary style transfer aims to synthesize a content image with the style of an image to create a third image that has never been seen before. Recent arbitrary style transfer algorithms find it challenging to balance the content structure and the style patterns. Moreover, simultaneously maintaining the global and local style patterns is difficult due to the patch-based mechanism. In this paper, we introduce a novel style-attentional network (SANet) that efficiently and flexibly integrates the local style patterns according to the semantic spatial distribution of the content image. A new identity loss function and multi-level feature embeddings enable our SANet and decoder to preserve the content structure as much as possible while enriching the style patterns. Experimental results demonstrate that our algorithm synthesizes stylized images in real-time that are higher in quality than those produced by the state-of-the-art algorithms.