Abstract
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