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.

翻译：数据效率，即从少量标注数据中泛化的能力，仍是深度学习中的主要挑战。半监督学习在传统识别任务中已成功减少对大量标注数据的需求，但在图像到图像翻译（I2I）任务中研究尚不充分。本文首次提出用于标签到图像翻译的半监督（半配对）框架，这是I2I中一个具有挑战性的子任务，旨在从语义标签图生成逼真图像。在半配对设置中，模型可访问少量配对数据及大量未配对的图像和标签。与先前工作利用几何变换作为前置任务不同，我们通过利用配对数据上的条件判别器作为反向生成器，采用输入重构任务。我们为这一共享网络提出训练算法，并设计稀有类别采样算法以关注代表性不足的类别。在三个标准基准上的实验表明，所提模型在使用更少配对样本的情况下，性能优于最先进的无监督、半监督方法及部分全监督方法。