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Recent image-to-image translation works have been transferred from supervisedto unsupervised settings due to the expensive cost of capturing or labelinglarge amounts of paired data. However, current unsupervised methods using thecycle-consistency constraint may not find the desired mapping, especially fordifficult translation tasks. On the other hand, a small number of paired dataare usually accessible. We therefore introduce a general framework forsemi-supervised image translation. Unlike previous works, our main idea is tolearn 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 desiredmapping function, resulting in improved quality of translation results as wellas the stability of the translation model. Empirically we show that usingfeature translation generates better results, even using a few bits of paireddata. Experimental comparisons with state-of-the-art approaches demonstrate theeffectiveness of the proposed framework on a variety of challengingimage-to-image translation tasks

Semi-Supervised Image-to-Image Translation using Latent Space Mapping