Universal Generative

Universal generative modeling aims to create flexible and powerful generative models capable of handling diverse data types and tasks. Current research focuses on integrating generative models with other techniques, such as regression models and Bayesian inference, to improve performance and robustness in applications like image restoration and MRI reconstruction. Prominent approaches include Bayesian Flow Networks and score-based generative models, often employed within unified frameworks that leverage information from multiple domains or representations. These advancements hold significant promise for improving the efficiency and quality of various image processing and medical imaging techniques.