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Data Augmentation Techniques with GenAI | Exclusive Lesson
Data Augmentation Techniques with GenAI | Exclusive Lesson
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 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%2C discussing its motivation%2C taxonomy%2C and key distinctions from synthetic data generation. We then systematically analyze the critical aspects of GDA - selection of generative models%2C techniques to utilize them%2C data selection methodologies%2C validation approaches%2C and diverse applications. Our proposed unified framework categorizes the extensive GDA literature%2C revealing gaps such as the lack of universal benchmarks. The thesis summarises promising research directions%2C including %2C effective data selection%2C theoretical development for large-scale models' application in GDA and establishing a benchmark for GDA. By laying a structured foundation%2C this thesis aims to nurture more cohesive development and accelerate progress in the vital arena of generative data augmentation.?resize=91,91 "A Unified Framework For Generative Data Augmentation: A Comprehensive ...")
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