  Image-to-image translation, which translates input images to a different
domain with a learned one-to-one mapping, has achieved impressive success in
recent years. The success of translation mainly relies on the network
architecture to reserve the structural information while modify the appearance
slightly at the pixel level through adversarial training. Although these
networks are able to learn the mapping, the translated images are predictable
without exclusion. It is more desirable to diversify them using image-to-image
translation by introducing uncertainties, i.e., the generated images hold
potential for variations in colors and textures in addition to the general
similarity to the input images, and this happens in both the target and source
domains. To this end, we propose a novel generative adversarial network (GAN)
based model, InjectionGAN, to learn a many-to-many mapping. In this model, the
input image is combined with latent variables, which comprise of
domain-specific attribute and unspecific random variations. The domain-specific
attribute indicates the target domain of the translation, while the unspecific
random variations introduce uncertainty into the model. A unified framework is
proposed to regroup these two parts and obtain diverse generations in each
domain. Extensive experiments demonstrate that the diverse generations have
high quality for the challenging image-to-image translation tasks where no
pairing information of the training dataset exits. Both quantitative and
qualitative results prove the superior performance of InjectionGAN over the
state-of-the-art approaches.
