  Unpaired image-to-image translation is a class of vision problems whose goal
is to find the mapping between different image domains using unpaired training
data. Cycle-consistency loss is a widely used constraint for such problems.
However, due to the strict pixel-level constraint, it cannot perform geometric
changes, remove large objects, or ignore irrelevant texture. In this paper, we
propose a novel adversarial-consistency loss for image-to-image translation.
This loss does not require the translated image to be translated back to be a
specific source image but can encourage the translated images to retain
important features of the source images and overcome the drawbacks of
cycle-consistency loss noted above. Our method achieves state-of-the-art
results on three challenging tasks: glasses removal, male-to-female
translation, and selfie-to-anime translation.
