AI generated summary of Pubmed citation/abstract with PMID 38091754

https://pubmed.ncbi.nlm.nih.gov/38091754

This study, “Towards performance-maximizing neural network pruning via global channel attention,” presents a novel static channel pruning method, GlobalPru, that outperforms existing state-of-the-art static and dynamic pruning methods on ImageNet, SVHN, and CIFAR-10/100 datasets by using a channel attention-based learn-to-rank framework to create a global ranking of channels and share it across different data.