  Image-to-image translation is a class of vision and graphics problems where
the goal is to learn the mapping between an input image and an output image
using a training set of aligned image pairs. However, for many tasks, paired
training data will not be available. We present an approach for learning to
translate an image from a source domain $X$ to a target domain $Y$ in the
absence of paired examples. Our goal is to learn a mapping $G: X \rightarrow Y$
such that the distribution of images from $G(X)$ is indistinguishable from the
distribution $Y$ using an adversarial loss. Because this mapping is highly
under-constrained, we couple it with an inverse mapping $F: Y \rightarrow X$
and introduce a cycle consistency loss to push $F(G(X)) \approx X$ (and vice
versa). Qualitative results are presented on several tasks where paired
training data does not exist, including collection style transfer, object
transfiguration, season transfer, photo enhancement, etc. Quantitative
comparisons against several prior methods demonstrate the superiority of our
approach.
