  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.
