Reverse Diffusion

Reverse diffusion models generate data by reversing a process that gradually adds noise to data until it becomes pure noise, then learning to reconstruct the original data from this noise. Current research focuses on improving the efficiency and accuracy of this reverse process, exploring various architectures like denoising diffusion probabilistic models (DDPMs) and incorporating techniques such as coarse-to-fine sampling, MAP estimation, and likelihood approximation to enhance performance. These advancements are significantly impacting diverse fields, enabling improved solutions to inverse problems in areas like image restoration, anomaly detection, and molecular relaxation, as well as accelerating sampling speeds and enhancing the quality of generated data.