Improving Style-Content Disentanglement in Image-to-Image Translation

Understand

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.