Iterative Neural Network

Iterative neural networks (INNs) are artificial neural networks whose outputs are fed back as inputs in a recurrent manner, enhancing performance through iterative refinement. Current research focuses on applying INNs to diverse problems, including inverse scattering, image processing (super-resolution, denoising, steganography), and solving partial differential equations, often incorporating techniques like variational methods, multigrid approaches, and attention mechanisms within specialized INN architectures. This iterative approach offers advantages in accuracy, efficiency, and uncertainty quantification compared to traditional methods, impacting fields ranging from medical imaging to computer vision and scientific computing.