Data Agnostic Filter Gating For Efficient Deep Networks
Hongyan Xu, Xiu Su, Shan You, Tao Huang, Fei Wang, Chen Qian, Changshui Zhang, Chang Xu
Abstract
Filter pruning is essential for deploying a well-trained CNN model on edge computation devices with a target computation budget (e.g., FLOPs). Current filter pruning methods mainly focus on leveraging feature maps to analyze the importance of filters, and prune those with less impact on the value of the CNN’s loss function, thereby ignoring the variance of input batches to differences in sparse structure over the filters. In this paper, we propose a data-agnostic filter pruning method that uses an auxiliary network named Dagger module to induce pruning with the pre-trained weights as input. Besides, to help prune filters with a preset FLOPs constraint, we utilize an explicit FLOPs-aware regularisation mechanism to directly promote pruning filters toward the target FLOPs. Experimental results on CIFAR-10 and ImageNet datasets show that the proposed filter pruning method surpasses the state-of-the-art.
BibTeX
@inproceedings{icassp2022_dataagnosticfilt,
title = {Data Agnostic Filter Gating For Efficient Deep Networks},
author = {Hongyan Xu and Xiu Su and Shan You and Tao Huang and Fei Wang and Chen Qian and Changshui Zhang and Chang Xu and Dadong Wang and Arcot Sowmya},
booktitle = {ICASSP 2022},
year = {2022}
}