Channel Pruning

Channel pruning is a neural network compression technique aiming to reduce model size and computational cost without significant accuracy loss by selectively removing less important channels (filters) within convolutional layers. Current research focuses on developing more efficient algorithms for identifying these channels, often employing techniques like information theory, meta-learning, and dynamic pruning strategies, and applying these methods to various architectures including convolutional neural networks (CNNs) and vision transformers (ViTs). This research is significant because it enables the deployment of deep learning models on resource-constrained devices, improving accessibility and efficiency in diverse applications ranging from image classification and object detection to natural language processing.