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Channel Pruning for Accelerating Very Deep Neural Networks
Open accessAbstract
In this paper, we introduce a new channel pruning method to accelerate very deep convolutional neural networks. Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel selection and least square reconstruction. We further generalize this algorithm to multi-layer and multi-branch cases. Our method reduces the accumulated error and enhance the compatibility with various architectures. Our pruned VGG-16 achieves the state-of-the-art results by 5× speed-up along with only 0.3% increase of error. More importantly, our method is able to accelerate modern networks like ResNet, Xception and suffers only 1.4%, 1.0% accuracy loss under 2× speedup respectively, which is significant.
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“However, the recent work [7] finds that the decomposition in [36] is even slower on GPU although it reduces FLOPs by 75%.”
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Metadata
- Authors
- He, Y., Zhang, X., & Sun, J.
- Affiliations
- Xi'an Jiaotong University (CN)
- Megvii (China) (CN)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1398-1406
- OpenAlex
- W2963363373
- Cited by
- 2,647
- References
- 59
- Citations by year
- 2026: 119 · 2025: 182 · 2024: 287 · 2023: 357 · 2022: 402 · 2021: 474 · 2020: 423 · 2019: 279 · 2018: 116 · 2017: 6
- FWCI
- 65.90
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- channel pruning, convolutional neural networks, model compression, LASSO, least-squares reconstruction, multi-branch network, VGG16, ResNet, Xception, inference acceleration
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