  Image-to-image translation tasks have been widely investigated with
Generative Adversarial Networks (GANs). However, existing approaches are mostly
designed in an unsupervised manner while little attention has been paid to
domain information within unpaired data. In this paper, we treat domain
information as explicit supervision and design an unpaired image-to-image
translation framework, Domain-supervised GAN (DosGAN), which takes the first
step towards the exploration of explicit domain supervision. In contrast to
representing domain characteristics using different generators or domain codes,
we pre-train a classification network to explicitly classify the domain of an
image. After pre-training, this network is used to extract the domain-specific
features of each image. Such features, together with the domain-independent
features extracted by another encoder (shared across different domains), are
used to generate image in target domain. Extensive experiments on multiple
facial attribute translation, multiple identity translation, multiple season
translation and conditional edges-to-shoes/handbags demonstrate the
effectiveness of our method. In addition, we can transfer the domain-specific
feature extractor obtained on the Facescrub dataset with domain supervision
information to unseen domains, such as faces in the CelebA dataset. We also
succeed in achieving conditional translation with any two images in CelebA,
while previous models like StarGAN cannot handle this task.
