Synthetic Data

Synthetic data generation aims to create artificial datasets that mimic the statistical properties of real-world data, addressing limitations like data scarcity, privacy concerns, and high annotation costs. Current research focuses on developing sophisticated generative models, including generative adversarial networks (GANs), energy-based models (EBMs), diffusion models, and masked language models, tailored to various data types (images, text, tabular data, audio). This rapidly evolving field significantly impacts diverse scientific domains and practical applications by enabling the training of robust machine learning models in situations where real data is insufficient or ethically problematic, ultimately improving model performance and expanding research possibilities.