With the popularity of mobile devices and the continuous growth of interactive data, FL
(Federated Learning) has gradually become an effective mean to address the problems of …

Filter pruning is widely used for neural network compression. However, existing methods
mostly judge importance of filters by measuring magnitudes or distributions of data in weight …

Filter pruning method introduces structural sparsity by removing selected filters and is thus
particularly effective for reducing complexity. Previous works empirically prune networks …

Channel pruning facilitates the acceleration and deployment of convolutional neural
networks on resource-constrained devices. Nevertheless, existing related methods mainly …

Modern deep learning architectures, while highly successful, are characterized by
substantial computational and memory demands due to their large number of parameters or …

Weight pruning is a well-known technique used for network compression. In contrast to filter
pruning, weight pruning produces higher compression ratios as it is more fine-grained …

Network pruning is effective in reducing memory usage and time complexity. However,
current approaches face two common limitations. 1) Pruned filters cannot contribute to the …

This study proposes a hybrid filter pruning method based on linear region analysis. Our
approach combines the advantages of cluster pruning and norm-based filter pruning by …

Pruning convolutional kernels is a crucial method for achieving model lightweighting.
However, current APoZ (Average Percentage of Zeros) based pruning algorithms often …