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DMCP: Differentiable Markov Channel Pruning for Neural Networks
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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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Li, Q., Li, H., & Meng, L. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01496-8 · Cited by 9
Self-citationLottery Jackpots Exist in Pre-Trained Models
Zhang, Y., Lin, M., Zhong, Y., Chao, F., & Ji, R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3311783 · Cited by 14
Xu, Z., Yu, F., Liu, C., Wu, Z., Wang, H., & Chen, X. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00369 · Cited by 9
Efficient deep-narrow residual networks using dilated pooling for scene recognition
Qiao, Z., Yuan, X., Zhang, R., Chen, T., & Zhang, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121018 · Cited by 8
Hua, J., Wang, Z., Han, H., Gao, H., & Nie, L. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad5199 · Cited by 8
Markov-PQ: Joint Pruning-Quantization via Learnable Markov Chain
Li, Y., Zhang, X., Xie, W., Zhang, J., Fang, L., & Du, J. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3437182 · Cited by 9
SIECP: Neural Network Channel Pruning based on Sequential Interval Estimation
Chen, S.-B., Zheng, Y., Ding, C. H. Q., & Luo, B. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.053 · Cited by 8
Channel Pruning via Multi-Criteria based on Weight Dependency
Yan, Y., Guo, R., Li, C., Yang, K., & Xu, Y. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534068 · Cited by 8
Automatic channel pruning by neural network based on improved poplar optimisation algorithm
Hu, Y., Chen, D., Zou, F., & Liu, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113002 · Cited by 7
Deep Learning Assisted mmWave Beam Prediction With Flexible Network Architecture
Wang, P., Ma, K., Bai, Y., Sun, C., & Wang, Z. (2025) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2025.3573327 · Cited by 7
Ding, Q., Li, W., Xu, C., Zhang, M., Sheng, C., He, M., & Shan, N. (2024) · Sensors · doi:10.3390/s24175789 · Cited by 7
Global balanced iterative pruning for efficient convolutional neural networks
Chang, J., Lu, Y., Xue, P., Xu, Y., & Wei, Z. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07594-7 · Cited by 7
Consecutive layer collaborative filter similarity for differentiable neural network pruning
Zu, X., Li, Y., & Yin, B. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.063 · Cited by 7
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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