@inproceedings{42ef4126677247cf81b73f638959442d,
title = "Towards Efficient Convolutional Neural Networks Through Low-Error Filter Saliency Estimation",
abstract = "Filter saliency based channel pruning is a state-of-the-art method for deep convolutional neural network compression and acceleration. This channel pruning method ranks the importance of individual filter by estimating its impact of each filter{\textquoteright}s removal on the training loss, and then remove the least important filters and fine-tune the remnant network. In this work, we propose a systematic channel pruning method that significantly reduces the estimation error of filter saliency. Different from existing approaches, our method largely reduces the magnitude of parameters in a network by introducing alternating direction method of multipliers (ADMM) into the pre-training procedure. Therefore, the estimation of filter saliency based on Taylor expansion is significantly improved. Extensive experiments with various benchmark network architectures and datasets demonstrate that the proposed method has a much improved unimportant filter selection capability and outperform state-of-the-art channel pruning method.",
keywords = "Alternating direction method of multipliers (ADMM), Efficient deep learning, Network pruning",
author = "Zi Wang and Chengcheng Li and Xiangyang Wang and Dali Wang",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019 ; Conference date: 26-08-2019 Through 30-08-2019",
year = "2019",
doi = "10.1007/978-3-030-29911-8_20",
language = "English",
isbn = "9783030299101",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "255--267",
editor = "Nayak, {Abhaya C.} and Alok Sharma",
booktitle = "PRICAI 2019",
}