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Machine Learning for Synthetic Data Generation: A Review

arxiv.org · 20,207 words · saved by 1 readers

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. Machine learning heavily relies on data, but real-world applications often encounter various data-related issues. These include data of poor quality, insufficient data points leading to under-fitting of machine learning models, and difficulties in data access due to concerns surrounding privacy, safety, and regulations. In light of these challenges, the concept of synthetic data generation emerges as a promising alternative that allows for data sharing and utilization in ways that real-world data cannot facilitate. This paper presents a comprehensive systematic review of existing studies that employ machine learning models for the purpose of generating synthetic data. The review encompasses various perspectives, starting with the applications of syntheti

Machine Learning for Synthetic Data Generation: A Review Yingzhou Lu1, Minjie Shen2, Huazheng Wang3, Xiao Wang4, Capucine van Rechem1, Wenqi Wei56 1Department of Pathology, Stanford University, Stanford, CA, 94305. 2 The Bradley Department of Electrical and Computer Engineering, Virginia Tech 3School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, 97331. 4 School of Computer Science & Engineering University of Washington, Seattle, WA, 98105. 6 Computer and Information Science Department, Fordham University, New York City, NY, 10023. 5Corresponding author

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