  It's useful to automatically transform an image from its original form to
some synthetic form (style, partial contents, etc.), while keeping the original
structure or semantics. We define this requirement as the "image-to-image
translation" problem, and propose a general approach to achieve it, based on
deep convolutional and conditional generative adversarial networks (GANs),
which has gained a phenomenal success to learn mapping images from noise input
since 2014. In this work, we develop a two step (unsupervised) learning method
to translate images between different domains by using unlabeled images without
specifying any correspondence between them, so that to avoid the cost of
acquiring labeled data. Compared with prior works, we demonstrated the capacity
of generality in our model, by which variance of translations can be conduct by
a single type of model. Such capability is desirable in applications like
bidirectional translation
