Towards Better Structured Pruning Saliency by Reorganizing Convolution
Xinglong Sun, Humphrey Shi
Abstract
We present SPSRC, a novel, simple and effective framework to extract enhanced Structured Pruning Saliency scores by Reorganizing Convolution. We observe that performance of pruning methods have gradually plateaued recently and propose to make better use of the learned convolutional kernel weights simply after a few steps of transformations. We firstly re-organize the convolutional operations between layers as matrix multiplications and then use the singular values and the matrix norms of the transformed matrices as saliency scores to decide what channels to prune or keep. We show both analytically and empirically that the long-standing kernel-norm-based channel importance measurement, like L1 magnitude, is not precise enough possessing a very obvious weakness of lacking spatial saliency but can be improved with SPSRC. We conduct extensive experiments across different settings and configurations and compare with the counterparts without our SPSRC along with other popular methods, observing obvious improvements. Our code is available at: https://github.com/AlexSunNik/SPSRC/tree/main.