A Unified Framework for Generative Data Augmentation: A Comprehensive
  Survey
Yunhao Chen Zihui Yan Yunjie Zhu
Generative data augmentation (GDA) has emerged as a promising technique to
alleviate data scarcity in machine learning applications. This thesis presents
a comprehensive survey and unified framework of the GDA landscape. We first
provide an overview of GDA, discussing its motivation, taxonomy, and key
distinctions from synthetic data generation. We then systematically analyze the
critical aspects of GDA - selection of generative models, techniques to utilize
them, data selection methodologies, validation approaches, and diverse
applications. Our proposed unified framework categorizes the extensive GDA
literature, revealing gaps such as the lack of universal benchmarks. The thesis
summarises promising research directions, including , effective data selection,
theoretical development for large-scale models' application in GDA and
establishing a benchmark for GDA. By laying a structured foundation, this
thesis aims to nurture more cohesive development and accelerate progress in the
vital arena of generative data augmentation.