The DOI matches its Crossref record; 22 of the 23 references it cites were found in a registry or an index.
DMCP: Differentiable Markov Channel Pruning for Neural Networks
Open accessAbstract
Recent works imply that the channel pruning can be regarded as searching optimal sub-structure from unpruned networks. However, existing works based on this observation require training and evaluating a large number of structures, which limits their application. In this paper, we propose a novel differentiable method for channel pruning, named Differentiable Markov Channel Pruning (DMCP), to efficiently search the optimal sub-structure. Our method is differentiable and can be directly optimized by gradient descent with respect to standard task loss and budget regularization (e.g. FLOPs constraint). In DMCP, we model the channel pruning as a Markov process, in which each state represents for retaining the corresponding channel during pruning, and transitions between states denote the pruning process. In the end, our method is able to implicitly select the proper number of channels in each layer by the Markov process with optimized transitions. To validate the effectiveness of our method, we perform extensive experiments on Imagenet with ResNet and MobilenetV2. Results show our method can achieve consistent improvement than state-of-the-art pruning methods in various FLOPs settings.
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“An unlearning procedure via channel pruning [97] was also provided, followed by a fine-tuning process to recover the performance of the pruned model.”
From Machine Unlearning: A Survey (2024) · cites this paper as [97]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755
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Liu, H., Simonyan, K., & Yang, Y. (2018) · arXiv · doi:10.48550/arxiv.1806.09055 · Cited by 3,248
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Li, H., Kadav, A., Durdanovic, I., Samet, H., & Graf, H. P. (2016) · arXiv · doi:10.48550/arxiv.1608.08710 · Cited by 2,928
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Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., & Zhang, C. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.298 · Cited by 2,761
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Cai, H., Zhu, L., & Han, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.00332 · Cited by 1,821
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A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations
Cheng, H., Zhang, M., & Shi, Q. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3447085 · Cited by 687
FFCA-YOLO for Small Object Detection in Remote Sensing Images
Zhang, Y., Ye, M., Zhu, G., Liu, Y., Guo, P., & Yan, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3363057 · Cited by 469
Self-citationStructured Pruning for Deep Convolutional Neural Networks: A Survey
He, Y., & Xiao, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3334614 · Cited by 468
Xu, H., Zhu, T., Zhang, L., Zhou, W., & Yu, P. S. (2024) · ACM Computing Surveys · doi:10.1145/3603620 · Cited by 164
“An unlearning procedure via channel pruning [97] was also provided, followed by a fine-tuning process to recover the performance of the pruned model.”
MethodStructured Pruning Learns Compact and Accurate Models
Xia, M., Zhong, Z., & Chen, D. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.107 · Cited by 136
Metadata
- Authors
- Guo, S., Wang, Y., Li, Q., & Yan, J.
- Affiliations
- Sensetime (China) (CN)
- ORCID
- Wang, Yujie
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 1536-1544
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035377608
- Cited by
- 198
- References
- 23
- Citations by year
- 2026: 4 · 2025: 25 · 2024: 43 · 2023: 32 · 2022: 46 · 2021: 43 · 2020: 5
- FWCI
- 9.72
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (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, Markov processes, neural network compression, ResNet, MobileNetV2
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