Diffusion Bridge

Diffusion bridges are stochastic processes designed to smoothly transition between two probability distributions, offering a powerful framework for generative modeling and solving inverse problems. Current research focuses on developing efficient algorithms, such as those based on Schrödinger bridges and score matching, to simulate these processes across various data types (images, audio, graphs, point clouds, time series) and model architectures, including diffusion models and neural networks. This approach has significant implications for diverse fields, enabling improved image restoration, molecular optimization, music timbre transfer, and more generally, facilitating the generation of high-quality samples from complex distributions.