  Unsupervised image-to-image translation methods have achieved tremendous
success in recent years. However, it can be easily observed that their models
contain significant entanglement which often hurts the translation performance.
In this work, we propose a principled approach for improving style-content
disentanglement in image-to-image translation. By considering the information
flow into each of the representations, we introduce an additional loss term
which serves as a content-bottleneck. We show that the results of our method
are significantly more disentangled than those produced by current methods,
while further improving the visual quality and translation diversity.
