Unsupervised image-to-image translation is used to transform images from a source domain to generate images in a target domain without using source-target image pairs. Promising results have been obtained for this problem in an adversarial setting using two independent GANs and attention mechanisms. We propose a new method that uses a single shared discriminator between the two GANs, which improves the overall efficacy. We assess the qualitative and quantitative results on image transfiguration, a cross-domain translation task, in a setting where the target domain shares similar semantics to the source domain. Our results indicate that even without adding attention mechanisms, our method performs at par with attention-based methods and generates images of comparable quality.
@article{arxiv.2102.04699,
title = {Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminator},
author = {Rajiv Kumar and Rishabh Dabral and G. Sivakumar},
journal= {arXiv preprint arXiv:2102.04699},
year = {2023}
}