Flow Based Model

Flow-based models are generative models that learn a transformation, or "flow," between a simple distribution (like a Gaussian) and a complex target distribution, enabling efficient sampling of realistic data. Current research emphasizes improving sampling speed and quality through techniques like stochastic sampling, velocity refinement, and the development of bespoke solvers for the underlying ordinary differential equations. These advancements are driving applications in diverse fields, including image generation, medical image synthesis, and scientific data analysis, where they offer advantages in speed, quality, and uncertainty quantification compared to alternative methods.