Synthetic Data Generation

Synthetic data generation aims to create artificial datasets that mimic the statistical properties of real data, addressing limitations in data availability, privacy concerns, and the high cost of data annotation. Current research focuses on developing advanced generative models, including diffusion models, generative adversarial networks, and methods leveraging large language models, to produce high-fidelity synthetic data across diverse data types (tabular, image, text, and even 3D models). This field is crucial for advancing machine learning in various domains, enabling the training of robust models in situations where real data is scarce, expensive, or sensitive, and improving the reliability and fairness of AI systems.