  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
