Pruning Strategy

Neural network pruning aims to reduce model size and computational cost without significant accuracy loss, improving efficiency and deployment on resource-constrained devices. Current research focuses on developing sophisticated pruning strategies, including structured and unstructured approaches, often employing techniques like magnitude-based ranking, mutual information analysis, and bi-level optimization, across various architectures such as convolutional neural networks (CNNs), vision transformers (ViTs), and even capsule networks. These advancements are crucial for deploying large-scale deep learning models in mobile and edge computing environments, and for enhancing model interpretability by identifying key features.