Lin, W., Ding, Y., Wei, H.-L. orcid.org/0000-0002-4704-7346 et al. (2 more authors) (2020) LdsConv : learned depthwise separable convolutions by group pruning. Sensors, 20 (15). 4349.
Abstract
Standard convolutional filters usually capture unnecessary overlap of features resulting in a waste of computational cost. In this paper, we aim to solve this problem by proposing a novel Learned Depthwise Separable Convolution (LdsConv) operation that is smart but has a strong capacity for learning. It integrates the pruning technique into the design of convolutional filters, formulated as a generic convolutional unit that can be used as a direct replacement of convolutions without any adjustments of the architecture. To show the effectiveness of the proposed method, experiments are carried out using the state-of-the-art convolutional neural networks (CNNs), including ResNet, DenseNet, SE-ResNet and MobileNet, respectively. The results show that by simply replacing the original convolution with LdsConv in these CNNs, it can achieve a significantly improved accuracy while reducing computational cost. For the case of ResNet50, the FLOPs can be reduced by 40.9%, meanwhile the accuracy on the associated ImageNet increases.
Metadata
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Copyright, Publisher and Additional Information: | © 2020 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | ||||
Keywords: | convolutional neural network; convolutional filter; classification | ||||
Dates: |
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Institution: | The University of Sheffield | ||||
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) | ||||
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Depositing User: | Symplectic Sheffield | ||||
Date Deposited: | 21 Aug 2020 10:18 | ||||
Last Modified: | 21 Aug 2020 10:18 | ||||
Status: | Published | ||||
Publisher: | MDPI AG | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.3390/s20154349 | ||||
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