Hierarchical Spatio-Channel Clustering for Efficient Model Compression in Medical Image Analysis

ArXi:2604.23375v1 Announce Type: new Convolutional neural networks (CNNs) have become increasingly difficult to deploy in resource-constrained environments due to their large memory and computational requirements. Although low-rank compression methods can reduce this burden, most existing approaches compress spatial and channel redundancy independently and. therefore. do not fully exploit the localised structure within convolutional feature maps.