Real Data

Real data, while crucial for training robust machine learning models, faces challenges related to acquisition cost, privacy concerns, and limited availability. Current research focuses on developing and evaluating synthetic data generation methods, employing various architectures like VAEs, GANs, and copulas, to create realistic surrogates for real data in diverse applications such as medical imaging, autonomous driving, and time-series analysis. This work aims to bridge the gap between synthetic and real data performance, focusing on metrics to assess synthetic data quality and techniques to mitigate biases introduced by synthetic data. The ultimate goal is to enable reliable and ethical model training while addressing data scarcity and privacy issues across numerous scientific and practical domains.