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.

本文提出了第一个半监督（半配对）的框架，用于标签到图像的翻译，使用输入重构任务并利用条件鉴别器来作为反向生成器，证明了所提出模型在标准基准测试中优于现有的无监督和半监督方法，同时使用更少的配对样本。