A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot
To address the limitation of conventional generative models (e.g., GANs, diffusion models) — their reliance on large-scale labeled data — in data-scarce domains such as medical imaging and remote sensing, this paper proposes a unified framework termed “Generative Modeling under Data Constraints” (GM-DC). We systematically establish a two-dimensional taxonomy: (i) task dimension—encompassing low-data, few-shot, and zero-shot settings; and (ii) methodological dimension—integrating transfer learning, meta-learning, prompt engineering, and multi-paradigm fusion. This work is the first to uncover cross-paradigm adaptation principles and synergistic mechanisms under data constraints. The survey comprehensively analyzes lightweight designs and knowledge transfer strategies for mainstream architectures—including VAEs, GANs, and diffusion models—and identifies critical research gaps while charting emerging trends. As the inaugural holistic GM-DC survey, it is accompanied by an open-source platform for continuous resource updates, providing both theoretical foundations and practical guidance for data-efficient generative modeling.