Score Based Diffusion

Score-based diffusion models are generative models that create data by reversing a diffusion process that gradually adds noise to data until it becomes pure noise. Current research focuses on improving these models' efficiency and accuracy through techniques like Wasserstein regularization for better calibration, generator-induced coupling for faster training of consistency models, and high-order score matching for improved likelihood estimation. These advancements are enabling applications across diverse fields, including high-resolution precipitation downscaling, video motion editing, anomaly detection in medical imaging, and high-quality audio and image synthesis, demonstrating the broad impact of score-based diffusion on data generation and analysis.