  Unsupervised image-to-image translation tasks aim to find a mapping between a
source domain X and a target domain Y from unpaired training data. Contrastive
learning for Unpaired image-to-image Translation (CUT) yields state-of-the-art
results in modeling unsupervised image-to-image translation by maximizing
mutual information between input and output patches using only one encoder for
both domains. In this paper, we propose a novel method based on contrastive
learning and a dual learning setting (exploiting two encoders) to infer an
efficient mapping between unpaired data. Additionally, while CUT suffers from
mode collapse, a variant of our method efficiently addresses this issue. We
further demonstrate the advantage of our approach through extensive ablation
studies demonstrating superior performance comparing to recent approaches in
multiple challenging image translation tasks. Lastly, we demonstrate that the
gap between unsupervised methods and supervised methods can be efficiently
closed.
