A comprehensive survey for generative data augmentation

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 k...

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Veröffentlicht in:Neurocomputing (Amsterdam) 2024-10, Vol.600, p.128167, Article 128167
Hauptverfasser: Chen, Yunhao, Yan, Zihui, Zhu, Yunjie
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Sprache:eng
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Zusammenfassung: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 summarizes 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. •Extensive and Latest Compilation: Drawing from 230 seminal works in the last three years, this survey presents the most comprehensive reviews on GDA.•Unified Framework Proposal: We introduce a structured GDA framework. This offers researchers a systematic guideline for improving and implementing GDA.•Deep Dive into Selection & Validation: Our survey delves deeply into the synthetic data selection and validation, which are given little attention on in the previous research works.•Future Roadmap: Benefitting from the extensive literature review, we discuss the existing challenges and potential breakthrough avenues.
ISSN:0925-2312
DOI:10.1016/j.neucom.2024.128167