Deep generative models, which target reproducing the given data distribution to produce novel samples, have made unprecedented advancements in recent years. Their technical breakthroughs have enabled unparalleled quality in the synthesis of visual content. However, one critical prerequisite for their tremendous success is the availability of a sufficient number of training samples, which requires massive computation resources. When trained on limited data, generative models tend to suffer from severe performance deterioration due to overfitting and memorization. Accordingly, researchers have devoted considerable attention to develop novel models that are capable of generating plausible and diverse images from limited training data recently. Despite numerous efforts to enhance training stability and synthesis quality in the limited data scenarios, there is a lack of a systematic survey that provides 1) a clear problem definition, critical challenges, and taxonomy of various tasks; 2) an in-depth analysis on the pros, cons, and remain limitations of existing literature; as well as 3) a thorough discussion on the potential applications and future directions in the field of image synthesis under limited data. In order to fill this gap and provide a informative introduction to researchers who are new to this topic, this survey offers a comprehensive review and a novel taxonomy on the development of image synthesis under limited data. In particular, it covers the problem definition, requirements, main solutions, popular benchmarks, and remain challenges in a comprehensive and all-around manner.

翻译：深度生成模型旨在复现给定数据分布以产生新样本，近年来取得了前所未有的进展。其技术突破使得视觉内容的合成质量达到了无与伦比的高度。然而，这些模型取得巨大成功的一个关键前提是拥有足量的训练样本，这需要庞大的计算资源。当在有限数据上训练时，生成模型往往因过拟合和记忆效应而导致性能严重下降。因此，研究人员近期投入大量精力开发能够从有限训练数据中生成合理且多样化图像的新型模型。尽管已有诸多努力旨在提升有限数据场景下的训练稳定性和合成质量，但目前仍缺乏系统的综述来提供：1) 清晰的问题定义、关键挑战及各类任务的分类体系；2) 对现有文献优缺点及遗留局限性的深入分析；以及 3) 对有限数据下图像合成领域潜在应用与未来方向的全面探讨。为填补这一空白，并向该领域的新研究人员提供详尽的引导，本综述对有限数据下图像合成的发展进行了全面回顾并提出了一种新颖的分类体系。具体而言，本文以全面、多维的方式涵盖了问题定义、需求条件、主要解决方案、常用基准测试以及遗留的挑战。