Diffusion Schr\"odinger Bridge

Diffusion Schrödinger Bridges (DSBs) are a class of generative models that leverage optimal transport theory to efficiently learn and sample from complex data distributions. Current research focuses on extending DSBs to various applications, including image denoising, point cloud processing, and medical image analysis, often incorporating neural networks and variational inference for improved scalability and performance. This approach offers advantages over traditional diffusion models by enabling faster sampling and incorporating prior knowledge or constraints, leading to improved accuracy and efficiency in diverse scientific and engineering domains. The resulting advancements are impacting fields ranging from medical imaging and materials science to robotics and climate modeling.