  Current unsupervised image-to-image translation techniques struggle to focus
their attention on individual objects without altering the background or the
way multiple objects interact within a scene. Motivated by the important role
of attention in human perception, we tackle this limitation by introducing
unsupervised attention mechanisms that are jointly adversarialy trained with
the generators and discriminators. We demonstrate qualitatively and
quantitatively that our approach is able to attend to relevant regions in the
image without requiring supervision, and that by doing so it achieves more
realistic mappings compared to recent approaches.
