Gaussian Diffusion

Gaussian diffusion models are generative models that learn to reverse a diffusion process, gradually removing noise from random data to generate realistic samples. Current research focuses on improving efficiency and control, including optimizing the diffusion process itself (e.g., using non-isotropic noise or alternative diffusion processes beyond Gaussian) and developing faster sampling techniques. These advancements are impacting diverse fields, from image and 3D shape generation and manipulation to spatiotemporal forecasting and even lossy compression, by offering powerful tools for data generation and analysis.