Conditional Invertible Neural Network

Conditional invertible neural networks (cINNs) are a class of neural networks designed to learn invertible mappings between input and output data, conditioned on additional information. Research focuses on applying cINNs to diverse inverse problems, leveraging their invertibility for tasks like probabilistic forecasting, image reconstruction (including super-resolution and domain transfer), and optimizing complex systems (e.g., retinal prosthetics, thin-film design). This approach offers advantages in efficiency and accuracy compared to traditional methods, particularly in scenarios with limited data or high dimensionality, impacting fields ranging from medical imaging to materials science and beyond. The ability to learn and reverse complex transformations makes cINNs a powerful tool for various scientific and engineering applications.