  Data efficiency, or the ability to generalize from a few labeled data,
remains a major challenge in deep learning. Semi-supervised learning has
thrived in traditional recognition tasks alleviating the need for large amounts
of labeled data, yet it remains understudied in image-to-image translation
(I2I) tasks. In this work, we introduce the first semi-supervised (semi-paired)
framework for label-to-image translation, a challenging subtask of I2I which
generates photorealistic images from semantic label maps. In the semi-paired
setting, the model has access to a small set of paired data and a larger set of
unpaired images and labels. Instead of using geometrical transformations as a
pretext task like previous works, we leverage an input reconstruction task by
exploiting the conditional discriminator on the paired data as a reverse
generator. We propose a training algorithm for this shared network, and we
present a rare classes sampling algorithm to focus on under-represented
classes. Experiments on 3 standard benchmarks show that the proposed model
outperforms state-of-the-art unsupervised and semi-supervised approaches, as
well as some fully supervised approaches while using a much smaller number of
paired samples.
