Unrolled Network

Unrolled networks are a class of neural networks constructed by iteratively unfolding optimization algorithms, offering increased interpretability and efficiency compared to traditional "black-box" models. Current research focuses on improving the robustness and convergence guarantees of these networks, exploring architectures based on iterative shrinkage, proximal methods, and graph-based approaches for tasks like image processing and multi-agent collaboration. This approach bridges the gap between optimization algorithms and deep learning, leading to more explainable models with improved performance in various applications, particularly in areas requiring efficient and robust solutions to inverse problems.