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：深度生成模型旨在再现给定数据分布以生成新样本，近年来取得了前所未有的进展。其技术突破使得视觉内容在合成质量上达到了卓越水平。然而，这些巨大成功的一个关键前提是需要大量训练样本，这需要庞大的计算资源。当在有限数据上训练时，生成模型往往会因过拟合和记忆化而导致性能严重退化。因此，研究人员近年来投入了大量精力，致力于开发能够从有限训练数据中生成合理且多样化图像的新模型。尽管在有限数据场景下提升训练稳定性和合成质量方面已做出诸多努力，但仍缺乏一个系统性的综述，提供：1）清晰的问题定义、关键挑战以及各类任务的分类体系；2）对现有文献优缺点及尚存局限性的深入分析；以及3）对有限数据下图像合成领域潜在应用和未来方向的全面讨论。为填补这一空白，并向刚涉足该领域的研究者提供翔实的入门介绍，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，它涵盖了问题定义、需求、主要解决方案、常用基准测试以及尚存挑战，以全面且多方位的方式进行了阐述。