1 INTRODUCTION

2 BACKGROUND  
2.1 Approaches for Generative Modeling  
2.2 Data Constraints and Commonly Used Datasets  

3 GENERATIVE MODELING UNDER DATA CONSTRAINT: TASK TAXONOMY, CHALLENGES  
3.1 Generative Modeling under Data Constraint: A Taxonomy on Tasks  
3.2 Generative Modeling under Data Constraint: Challenges  

4 COMPREHENSIVE REVIEW  
4.1 Transfer Learning  
4.2 Data Augmentation  
4.3 Network Architectures  
4.4 Multi-Task Objectives  
4.5 Exploiting Frequency Components  
4.6 Meta-Learning  
4.7 Modeling Internal Patch Distribution  

5 DISCUSSION  
5.1 Analysis of the Research Landscape  
5.2 Research Gap and Future Directions  
5.3 Beyond Image Generation  

6 CONCLUSION