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[2307.14397] A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot
Summary
This survey explores generative modeling under constraints of limited data, few shots, and zero shots, presenting challenges and methodologies to enhance model performance in real-world applications.
Why It Matters
As generative modeling becomes increasingly relevant in fields like medicine and satellite imaging, understanding how to effectively generate data with limited resources is crucial. This survey provides a comprehensive overview of current challenges and methodologies, guiding researchers and practitioners towards more effective solutions.
Key Takeaways
- Generative modeling faces significant challenges under limited data conditions, including overfitting and frequency bias.
- The survey introduces two novel taxonomies for categorizing tasks and methodological approaches in generative modeling.
- Over 230 papers are reviewed, offering insights into various generative model types and their performance under constraints.
- Future directions include adapting foundation models and developing holistic evaluation frameworks.
- The findings serve as a roadmap for advancing generative modeling techniques in real-world applications.
Computer Science > Computer Vision and Pattern Recognition arXiv:2307.14397 (cs) [Submitted on 26 Jul 2023 (v1), last revised 14 Feb 2026 (this version, v3)] Title:A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot Authors:Milad Abdollahzadeh, Guimeng Liu, Touba Malekzadeh, Christopher T. H. Teo, Keshigeyan Chandrasegaran, Ngai-Man Cheung View a PDF of the paper titled A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot, by Milad Abdollahzadeh and 5 other authors View PDF HTML (experimental) Abstract:Generative modeling in machine learning aims to synthesize new data samples that are statistically similar to those observed during training. While conventional generative models such as GANs and diffusion models typically assume access to large and diverse datasets, many real-world applications (e.g. in medicine, satellite imaging, and artistic domains) operate under limited data availability and strict constraints. In this survey, we examine Generative Modeling under Data Constraint (GM-DC), which includes limited-data, few-shot, and zero-shot settings. We present a unified perspective on the key challenges in GM-DC, including overfitting, frequency bias, and incompatible knowledge transfer, and discuss how these issues impact model performance. To systematically analyze this growing field, we introduce two novel taxonomies: one categorizing GM-DC tasks (e.g. unconditional vs. conditional generation, cross-domain adaptation, a...
