Diffusion Probabilistic Model

Diffusion probabilistic models are generative models that create new data by reversing a noise-adding diffusion process, aiming to learn the underlying data distribution. Current research focuses on improving sampling efficiency through novel algorithms and architectures like ODE solvers and training-free methods, as well as adapting these models to diverse tasks such as image classification, video segmentation, and scientific computing problems. Their ability to generate high-quality samples and quantify uncertainty makes them significant tools across various fields, impacting areas from medical image analysis and aerodynamic design to time series forecasting and material science.