  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.
