  Recently, a unified model for image-to-image translation tasks within
adversarial learning framework has aroused widespread research interests in
computer vision practitioners. Their reported empirical success however lacks
solid theoretical interpretations for its inherent mechanism. In this paper, we
reformulate their model from a brand-new geometrical perspective and have
eventually reached a full interpretation on some interesting but unclear
empirical phenomenons from their experiments. Furthermore, by extending the
definition of generalization for generative adversarial nets to a broader
sense, we have derived a condition to control the generalization capability of
their model. According to our derived condition, several practical suggestions
have also been proposed on model design and dataset construction as a guidance
for further empirical researches.
