Driven Network

Driven networks leverage data and established models to improve the performance of complex systems. Current research focuses on integrating diverse model priors within deep learning architectures, such as collaborative networks and unfolding networks, to enhance accuracy and generalization in applications like image reconstruction and traffic flow prediction. These approaches offer significant advantages over traditional methods by incorporating prior knowledge and learning directly from data, leading to faster, more accurate solutions for various problems. The resulting improvements in efficiency and accuracy have broad implications across diverse fields, including medical imaging and transportation systems.