Score Based Diffusion Model

Score-based diffusion models are generative models that learn to reverse a diffusion process, transforming noise into data samples from a target distribution. Current research focuses on improving the efficiency and theoretical understanding of these models, including developing training-free methods, analyzing convergence rates under various assumptions, and exploring different model architectures for specific data types like time series and graphs. These advancements are significant because they enable the generation of high-quality samples across diverse domains, improving applications ranging from image generation and medical imaging to trajectory planning and signal processing.