# Unsupervised Image-to-Image Translation with Self-Attention Networks

## TL;DR

This paper introduces an unsupervised image-to-image translation method leveraging self-attention networks to better capture geometric changes and details, outperforming existing methods in quality and accuracy.

## Contribution

It is the first to verify and utilize self-attention networks for unsupervised image translation, enhancing geometric and detail preservation.

## Key findings

- Outperforms state-of-the-art methods in qualitative assessments
- Achieves higher quantitative scores in image translation tasks
- Effectively captures complex geometric and structural changes

## Abstract

Unsupervised image translation aims to learn the transformation from a source domain to another target domain given unpaired training data. Several state-of-the-art works have yielded impressive results in the GANs-based unsupervised image-to-image translation. It fails to capture strong geometric or structural changes between domains, or it produces unsatisfactory result for complex scenes, compared to local texture mapping tasks such as style transfer. Recently, SAGAN (Han Zhang, 2018) showed that the self-attention network produces better results than the convolution-based GAN. However, the effectiveness of the self-attention network in unsupervised image-to-image translation tasks have not been verified. In this paper, we propose an unsupervised image-to-image translation with self-attention networks, in which long range dependency helps to not only capture strong geometric change but also generate details using cues from all feature locations. In experiments, we qualitatively and quantitatively show superiority of the proposed method compared to existing state-of-the-art unsupervised image-to-image translation task. The source code and our results are online: https://github.com/itsss/img2img_sa and http://itsc.kr/2019/01/24/2019_img2img_sa