Machine Learning for Synthetic Data Generation: A Review

Understand

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.