Flow Prior

Flow priors leverage the power of normalizing flows, a type of generative model, to improve various downstream tasks by incorporating learned probability distributions as prior knowledge. Current research focuses on applying flow priors to enhance image generation, inverse problems (like super-resolution and denoising), and video processing, often comparing their performance against diffusion models. This approach offers advantages in efficiency and sample quality, particularly for high-dimensional data, leading to improved results in diverse applications ranging from medical imaging to expressive speech synthesis. The ability to efficiently model complex data distributions makes flow priors a valuable tool across numerous scientific fields.