Generative Prior

Generative priors leverage the knowledge encoded in pre-trained generative models, such as GANs and diffusion models, to improve various downstream tasks. Current research focuses on integrating these priors into diverse applications, including inverse problems (e.g., image deconvolution, MRI reconstruction), and enhancing existing algorithms like expectation-maximization and gradient descent through informed initialization and regularization. This approach offers significant advantages by improving efficiency, accuracy, and robustness, particularly in scenarios with limited data or high dimensionality, impacting fields ranging from medical imaging to computer graphics.