Invertible Neural Network

Invertible neural networks (INNs) are a class of neural networks designed with inherent invertibility, allowing for efficient computation of both forward and inverse mappings. Current research focuses on applying INNs to diverse inverse problems, leveraging their ability to avoid information loss and enable bidirectional learning, with architectures like coupling flows and normalizing flows being prominent. This capability has significant implications across various fields, including image compression, medical imaging, and robotics, by enabling more accurate and efficient solutions to challenging inverse problems where traditional methods struggle. The development of INN-based methods is improving the performance of many applications by offering better accuracy, robustness, and efficiency.