Score Estimation Network

Score estimation networks are crucial components of score-based generative models, aiming to efficiently learn and estimate the score function (gradient of the log-probability density) of a data distribution. Current research focuses on improving sampling speed through techniques like early exiting and score normalization within algorithms such as the Diffusion Exponential Integrator Sampler (DEIS), as well as enhancing model robustness and accuracy via uncertainty quantification and high-order score matching. These advancements are significant because they lead to faster and more reliable generation of high-quality samples, impacting various applications from image synthesis to computer vision tasks like pose estimation.