Conditional Flow Matching

Conditional Flow Matching (CFM) is a family of training algorithms for continuous normalizing flows (CNFs) that aims to efficiently learn the mapping between a simple prior distribution and a complex target distribution. Research currently focuses on improving CFM's speed and sample quality through modifications like incorporating Gaussian processes, implicit dynamics, and optimal transport, leading to models that are faster and more accurate than previous approaches. These advancements have yielded significant improvements in various applications, including audio and image generation, robotic control, and time-series modeling, demonstrating CFM's versatility and potential for broader impact across diverse scientific and engineering domains.