  Image-to-image translation tasks have been widely investigated with
Generative Adversarial Networks (GANs) and dual learning. However, existing
models lack the ability to control the translated results in the target domain
and their results usually lack of diversity in the sense that a fixed image
usually leads to (almost) deterministic translation result. In this paper, we
study a new problem, conditional image-to-image translation, which is to
translate an image from the source domain to the target domain conditioned on a
given image in the target domain. It requires that the generated image should
inherit some domain-specific features of the conditional image from the target
domain. Therefore, changing the conditional image in the target domain will
lead to diverse translation results for a fixed input image from the source
domain, and therefore the conditional input image helps to control the
translation results. We tackle this problem with unpaired data based on GANs
and dual learning. We twist two conditional translation models (one translation
from A domain to B domain, and the other one from B domain to A domain)
together for inputs combination and reconstruction while preserving domain
independent features. We carry out experiments on men's faces from-to women's
faces translation and edges to shoes&bags translations. The results demonstrate
the effectiveness of our proposed method.
