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Approximated oracle filter pruning for destructive CNN width optimization
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Ding, Xiaohan, Ding, Guiguang, Guo, Yuchen, Han, Jungong and Yan, Chenggang (2019) Approximated oracle filter pruning for destructive CNN width optimization. In: ICML 2019 ; 36th International Conference on Machine Learning, California, 10-15 Jun 2019. Published in: Proceedings of the 36th International Conference on Machine Learning
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Official URL: http://proceedings.mlr.press/v97/
Abstract
It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to remove the unimportant filters from a well-trained CNN, which estimates the filters’ importance by ablating them in turn and evaluating the model, thus delivers high accuracy but suffers from intolerable time complexity, and requires a given resulting width but cannot automatically find it. To address these problems, we propose Approximated Oracle Filter Pruning (AOFP), which keeps searching for the least important filters in a binary search manner, makes pruning attempts by masking out filters randomly, accumulates the resulting errors, and finetunes the model via a multi-path framework. As AOFP enables simultaneous pruning on multiple layers, we can prune an existing very deep CNN with acceptable time cost, negligible accuracy drop, and no heuristic knowledge, or re-design a model which exerts higher accuracy and faster inference
Item Type: | Conference Item (Paper) | |||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | |||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Neural networks (Computer science), Computer vision, Machine learning, Artificial intelligence -- Computer programs | |||||||||||||||
Journal or Publication Title: | Proceedings of the 36th International Conference on Machine Learning | |||||||||||||||
Official Date: | June 2019 | |||||||||||||||
Dates: |
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Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Date of first compliant deposit: | 25 June 2019 | |||||||||||||||
Date of first compliant Open Access: | 1 July 2019 | |||||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||||||||
Title of Event: | ICML 2019 ; 36th International Conference on Machine Learning | |||||||||||||||
Type of Event: | Conference | |||||||||||||||
Location of Event: | California | |||||||||||||||
Date(s) of Event: | 10-15 Jun 2019 | |||||||||||||||
Open Access Version: |
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