AI RESEARCH
A Greedy Hierarchical Approach to Whole-Network Filter-Pruning in CNNs
arXiv CS.LG
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ArXi:2409.03777v3 Announce Type: replace-cross Deep convolutional neural networks (CNNs) have achieved impressive performance in many computer vision tasks. However, their large model sizes require heavy computational resources, making pruning redundant filters from existing pre-trained CNNs an essential task in developing efficient models for resource-constrained devices. Whole-network filter pruning algorithms prune varying fractions of filters from each layer, hence providing greater flexibility.