Implicit Prior

Implicit priors represent a powerful approach in machine learning and related fields, aiming to incorporate prior knowledge or assumptions into models without explicitly defining a probability distribution. Current research focuses on leveraging implicit priors within various architectures, including generative adversarial networks (GANs), energy-based models (EBMs), and diffusion models, often combined with techniques like plug-and-play methods and variational inference for efficient learning and inference. This approach enhances model performance in diverse applications, such as image super-resolution, robotic grasping, and Bayesian inference in high-dimensional spaces, by improving accuracy, uncertainty quantification, and handling of limited data. The ability to effectively incorporate prior knowledge implicitly promises significant advancements across numerous scientific domains and practical applications.