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http://hdl.handle.net/1942/33444
Title: | Fine-Grained Channel Pruning for Deep Residual Neural Networks | Authors: | CHEN, Siang Huang, Kai Xiong, Dongliang LI, Bowen CLAESEN, Luc |
Issue Date: | 2020 | Publisher: | SPRINGER INTERNATIONAL PUBLISHING AG | Source: | Lecture notes in computer science, 12397 , p. 3 -14 | Abstract: | Pruning residual neural networks is a challenging task due to the constraints induced by cross layer connections. Many existing approaches assign channels connected by skip-connections to the same group and prune them simultaneously, limiting the pruning ratio on those troublesome filters. Instead, we propose a Fine-grained Channel Pruning (FCP) method that allows any channels to be pruned independently. To avoid the misalignment problem between convolution and skip connection , we always keep the residual addition operations alive. Thus we can obtain a novel efficient residual architecture by removing any unimportant channels without the alignment constraint. Besides classification, We further apply FCP on residual models for image super-resolution, which is a low-level vision task. Extensive experimental results show that FCP can achieve better performance than other state-of-the-art methods in terms of parameter and computation cost. Notably, on CIFAR-10, FCP reduces more than 78% FLOPs on ResNet-56 with no accuracy drop. Moreover, it achieves more than 48% FLOPs reduction on MSR-ResNet with negligible performance degradation. | Keywords: | Channel pruning;Residual neural network;Efficient network structure | Document URI: | http://hdl.handle.net/1942/33444 | ISBN: | 978-3-030-61615-1 | ISSN: | 0302-9743 | DOI: | 10.1007/978-3-030-61616-8_1 | ISI #: | 000713797800001 | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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Fine-grained Channel Pruning for Deep Residual Neural Networks.pdf | Peer-reviewed author version | 494.04 kB | Adobe PDF | View/Open |
Fine-Grained Channel Pruning for Deep Residual Neural Networks.pdf Restricted Access | Published version | 695.06 kB | Adobe PDF | View/Open Request a copy |
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