  Recent image-to-image translation works have been transferred from supervised
to unsupervised settings due to the expensive cost of capturing or labeling
large amounts of paired data. However, current unsupervised methods using the
cycle-consistency constraint may not find the desired mapping, especially for
difficult translation tasks. On the other hand, a small number of paired data
are usually accessible. We therefore introduce a general framework for
semi-supervised image translation. Unlike previous works, our main idea is to
learn the translation over the latent feature space instead of the image space.
Thanks to the low dimensional feature space, it is easier to find the desired
mapping function, resulting in improved quality of translation results as well
as the stability of the translation model. Empirically we show that using
feature translation generates better results, even using a few bits of paired
data. Experimental comparisons with state-of-the-art approaches demonstrate the
effectiveness of the proposed framework on a variety of challenging
image-to-image translation tasks
