no code implementations • CVPR 2021 • Zi Wang, Chengcheng Li, Xiangyang Wang
Based on this finding, we then propose a network pruning approach that identifies structural redundancy of a CNN and prunes filters in the selected layer(s) with the most redundancy.
no code implementations • 16 May 2019 • Chengcheng Li, Zi Wang, Dali Wang, Xiangyang Wang, Hairong Qi
Most existing channel pruning methods formulate the pruning task from a perspective of inefficiency reduction which iteratively rank and remove the least important filters, or find the set of filters that minimizes some reconstruction errors after pruning.
no code implementations • 18 Feb 2019 • Zi Wang, Chengcheng Li, Dali Wang, Xiangyang Wang, Hairong Qi
In specific, with the proposed method, 75% and 54% of the total computation time for the whole pruning procedure can be reduced for AlexNet on CIFAR-10, and for VGG-16 on ImageNet, respectively.
no code implementations • 18 Feb 2019 • Chengcheng Li, Zi Wang, Xiangyang Wang, Hairong Qi
In this work, we propose a novel single-shot channel pruning approach based on alternating direction methods of multipliers (ADMM), which can eliminate the need for complex iterative pruning and fine-tuning procedure and achieve a target compression ratio with only one run of pruning and fine-tuning.