A Unified Framework for Generative Data Augmentation: A Comprehensive Survey
It addresses data scarcity for machine learning practitioners, but is incremental as it synthesizes existing literature without introducing new methods.
This thesis tackles the problem of data scarcity in machine learning by providing a comprehensive survey and unified framework for generative data augmentation, aiming to structure the field and identify research gaps.
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.