Synthetic Data Generator

Synthetic data generators are computational tools designed to create artificial datasets that mimic the statistical properties of real-world data while addressing issues like data scarcity, privacy concerns, and bias. Current research emphasizes developing generators that accurately reflect complex data structures, particularly in tabular and time-series data, often employing deep learning architectures like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), as well as Markov processes. These generators are proving valuable for improving model performance in low-data regimes, enhancing fairness in machine learning, and enabling research in areas with limited access to real data, such as healthcare and biometrics. Rigorous evaluation frameworks are also being developed to ensure the quality and reliability of synthetic data for downstream applications.