Synthetic Sample

Synthetic sample generation is a rapidly evolving field focused on creating artificial data that mirrors the statistical properties of real datasets, addressing data scarcity, privacy concerns, and noisy labels. Current research emphasizes generative models, including GANs and diffusion models, often enhanced by techniques like data-free distillation, coreset selection, and entropic regularization to improve synthetic data quality and utility in downstream tasks. This work has significant implications for various applications, including improving the performance of machine learning models trained on limited or sensitive data, and enabling more robust and efficient anomaly detection in diverse fields like medical imaging and industrial quality control.