  Despite remarkable success in unpaired image-to-image translation, existing
systems still require a large amount of labeled images. This is a bottleneck
for their real-world applications; in practice, a model trained on labeled
CelebA dataset does not work well for test images from a different distribution
-- greatly limiting their application to unlabeled images of a much larger
quantity. In this paper, we attempt to alleviate this necessity for labeled
data in the facial image translation domain. We aim to explore the degree to
which you can discover novel attributes from unlabeled faces and perform
high-quality translation. To this end, we use prior knowledge about the visual
world as guidance to discover novel attributes and transfer them via a novel
normalization method. Experiments show that our method trained on unlabeled
data produces high-quality translations, preserves identity, and be
perceptually realistic as good as, or better than, state-of-the-art methods
trained on labeled data.
