  Image-to-image translation is to convert an image of the certain style to
another of the target style with the content preserved. A desired translator
should be capable to generate diverse results in a controllable (many-to-many)
fashion. To this end, we design a novel generative adversarial network, namely
exemplar-domain aware image-to-image translator (EDIT for short). The principle
behind is that, for images from multiple domains, the content features can be
obtained by a uniform extractor, while (re-)stylization is achieved by mapping
the extracted features specifically to different purposes (domains and
exemplars). The generator of our EDIT comprises of a part of blocks configured
by shared parameters, and the rest by varied parameters exported by an
exemplar-domain aware parameter network. In addition, a discriminator is
equipped during the training phase to guarantee the output satisfying the
distribution of the target domain. Our EDIT can flexibly and effectively work
on multiple domains and arbitrary exemplars in a unified neat model. We conduct
experiments to show the efficacy of our design, and reveal its advances over
other state-of-the-art methods both quantitatively and qualitatively.
