  In image-to-image translation the goal is to learn a mapping from one image
domain to another. In the case of supervised approaches the mapping is learned
from paired samples. However, collecting large sets of image pairs is often
either prohibitively expensive or not possible. As a result, in recent years
more attention has been given to techniques that learn the mapping from
unpaired sets.
  In our work, we show that injecting implicit pairs into unpaired sets
strengthens the mapping between the two domains, improves the compatibility of
their distributions, and leads to performance boosting of unsupervised
techniques by over 14% across several measurements.
  The competence of the implicit pairs is further displayed with the use of
pseudo-pairs, i.e., paired samples which only approximate a real pair. We
demonstrate the effect of the approximated implicit samples on image-to-image
translation problems, where such pseudo-pairs may be synthesized in one
direction, but not in the other. We further show that pseudo-pairs are
significantly more effective as implicit pairs in an unpaired setting, than
directly using them explicitly in a paired setting.
