  Synthetic image translation has significant potentials in autonomous
transportation systems. That is due to the expense of data collection and
annotation as well as the unmanageable diversity of real-words situations. The
main issue with unpaired image-to-image translation is the ill-posed nature of
the problem. In this work, we propose a novel method for constraining the
output space of unpaired image-to-image translation. We make the assumption
that the environment of the source domain is known (e.g. synthetically
generated), and we propose to explicitly enforce preservation of the
ground-truth labels on the translated images.
  We experiment on preserving ground-truth information such as semantic
segmentation, disparity, and instance segmentation. We show significant
evidence that our method achieves improved performance over the
state-of-the-art model of UNIT for translating images from SYNTHIA to
Cityscapes. The generated images are perceived as more realistic in human
surveys and outperforms UNIT when used in a domain adaptation scenario for
semantic segmentation.
