A Unified Framework for Generative Data Augmentation: A Comprehensive Survey

arXiv:2310.00277v2 Announce Type: replace
Abstract: 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. …

abstract applications arxiv augmentation cs.ai cs.lg data framework gda generative key landscape machine machine learning machine learning applications motivation overview survey taxonomy thesis type