Pruning Method

Neural network pruning aims to reduce model size and computational cost without significant performance loss, primarily focusing on improving efficiency and resource utilization. Current research explores various pruning strategies, including unstructured and structured approaches applied to convolutional neural networks (CNNs), transformers, and large language models (LLMs), often incorporating techniques like knowledge distillation and Bayesian methods to enhance accuracy and speed. These advancements are significant for deploying deep learning models on resource-constrained devices and accelerating inference times, impacting both scientific research and practical applications across diverse fields.