Guo, J.Y.[Jin-Yang],
Zhang, W.C.[Wei-Chen],
Ouyang, W.L.[Wan-Li],
Xu, D.[Dong],
Model Compression Using Progressive Channel Pruning,
CirSysVideo(31), No. 3, March 2021, pp. 1114-1124.
IEEE DOI
2103
Acceleration, Adaptation models, Convolution, Supervised learning,
Neural networks, Computational modeling, Model compression,
transfer learning
BibRef
Liu, Y.X.[Yi-Xin],
Guo, Y.[Yong],
Guo, J.X.[Jia-Xin],
Jiang, L.Q.[Luo-Qian],
Chen, J.[Jian],
Conditional Automated Channel Pruning for Deep Neural Networks,
SPLetters(28), 2021, pp. 1275-1279.
IEEE DOI
2107
Computational modeling, Image coding, Optimization,
Markov processes, Signal processing algorithms, Search problems,
model compression
BibRef
Liu, J.[Jing],
Zhuang, B.[Bohan],
Zhuang, Z.W.[Zhuang-Wei],
Guo, Y.[Yong],
Huang, J.Z.[Jun-Zhou],
Zhu, J.H.[Jin-Hui],
Tan, M.K.[Ming-Kui],
Discrimination-Aware Network Pruning for Deep Model Compression,
PAMI(44), No. 8, August 2022, pp. 4035-4051.
IEEE DOI
2207
Kernel, Computational modeling, Quantization (signal), Training,
Adaptation models, Acceleration, Redundancy, Channel pruning,
deep neural networks
BibRef
Ye, H.C.[Han-Cheng],
Zhang, B.[Bo],
Chen, T.[Tao],
Fan, J.Y.[Jia-Yuan],
Wang, B.[Bin],
Performance-Aware Approximation of Global Channel Pruning for
Multitask CNNs,
PAMI(45), No. 8, August 2023, pp. 10267-10284.
IEEE DOI
2307
Task analysis, Information filters, Adaptation models,
Analytical models, Predictive models, Sensitivity,
sequentially greedy algorithm
BibRef
Zhao, C.L.[Cheng-Long],
Zhang, Y.X.[Yun-Xiang],
Ni, B.B.[Bing-Bing],
Exploiting Channel Similarity for Network Pruning,
CirSysVideo(33), No. 9, September 2023, pp. 5049-5061.
IEEE DOI
2310
BibRef
Pan, J.H.[Jian-Hong],
Yang, S.Y.[Si-Yuan],
Foo, L.G.[Lin Geng],
Ke, Q.H.[Qiu-Hong],
Rahmani, H.[Hossein],
Fan, Z.P.[Zhi-Peng],
Liu, J.[Jun],
Progressive Channel-Shrinking Network,
MultMed(26), 2024, pp. 2016-2026.
IEEE DOI Code:
WWW Link.
2402
Training, Indexing, Convolution, Costs, Generators, Feature extraction,
Testing, Progressive, network shrinking
BibRef
Kim, N.J.[Nam Joon],
Kim, H.[Hyun],
Trunk Pruning: Highly Compatible Channel Pruning for Convolutional
Neural Networks Without Fine-Tuning,
MultMed(26), 2024, pp. 5588-5599.
IEEE DOI
2404
Training, Kernel, Scalability, Channel estimation, Taylor series,
Probabilistic logic, Indexes, Convolutional Neural Network (CNN),
Fine-Tuning
BibRef
Hu, F.[Fuyi],
Zhang, J.[Jin],
Gao, S.[Song],
Lin, Y.[Yu],
Zhou, W.[Wei],
Wang, R.[Ruxin],
An efficient training-from-scratch framework with BN-based structural
compressor,
PR(153), 2024, pp. 110546.
Elsevier DOI
2405
Channel pruning, Model compression, Knowledge distillation,
Convolutional Neural Network (CNN)
BibRef
Cheng, H.R.[Hong-Rong],
Zhang, M.[Miao],
Shi, J.Q.F.[Javen Qin-Feng],
Influence Function Based Second-Order Channel Pruning:
Evaluating True Loss Changes for Pruning is Possible Without Retraining,
PAMI(46), No. 12, December 2024, pp. 9023-9037.
IEEE DOI
2411
Neural networks, Computational modeling, Accuracy, Training,
Optimization, Classification algorithms, Task analysis, model compression
BibRef
Ma, M.[Ming],
Zhang, T.Z.[Tong-Zhou],
Wang, Z.M.[Zi-Ming],
Wang, Y.[Yue],
Du, T.[Taoli],
Li, W.H.[Wen-Hui],
Global Channel Pruning With Self-Supervised Mask Learning,
CirSysVideo(35), No. 3, March 2025, pp. 2013-2025.
IEEE DOI
2503
Self-supervised learning, Training, Filters, Sparse matrices,
Supervised learning, Neural networks,
self-supervised learning
BibRef
Ji, F.[Fengrui],
Chen, X.W.[Xin-Wang],
Chu, R.[Renxin],
Liu, B.L.[Bao-Lin],
Network slimming using Lp(p<1) regularization,
PR(167), 2025, pp. 111711.
Elsevier DOI
2506
Channel pruning, L (p < 1) regularization,
Alternating direction method of multipliers, Efficient inference
BibRef
Li, Y.[Ye],
Tang, C.[Chen],
Meng, Y.[Yuan],
Fan, J.J.[Jia-Jun],
Chai, Z.H.[Zeng-Hao],
Ma, X.Z.[Xin-Zhu],
Wang, Z.[Zhi],
Zhu, W.W.[Wen-Wu],
PRANCE: Joint Token-Optimization and Structural Channel-Pruning for
Adaptive ViT Inference,
PAMI(48), No. 1, January 2026, pp. 283-298.
IEEE DOI
2512
Optimization, Transformers, Data models, Training,
Computational modeling, Accuracy, Merging, Complexity theory,
model lightweight
BibRef
Gao, S.Q.[Shang-Qian],
Zhang, Y.[Yanfu],
Huang, F.H.[Fei-Hu],
Huang, H.[Heng],
BilevelPruning: Unified Dynamic and Static Channel Pruning for
Convolutional Neural Networks,
CVPR24(16090-16100)
IEEE DOI
2410
Costs, Runtime, Convolutional neural networks, Optimization
BibRef
Huang, Y.[Yaomin],
Liu, N.[Ning],
Che, Z.P.[Zheng-Ping],
Xu, Z.Y.[Zhi-Yuan],
Shen, C.M.[Chao-Min],
Peng, Y.X.[Ya-Xin],
Zhang, G.X.[Gui-Xu],
Liu, X.[Xinmei],
Feng, F.F.[Fei-Fei],
Tang, J.[Jian],
CP3: Channel Pruning Plug-in for Point-Based Networks,
CVPR23(5302-5312)
IEEE DOI
2309
BibRef
Long, X.[Xin],
Zeng, X.R.[Xiang-Rong],
Liu, Y.[Yu],
Qiao, M.[Mu],
Low Bit Neural Networks with Channel Sparsity and Sharing,
ICIVC22(889-894)
IEEE DOI
2301
Training, Visualization, Tensors, Quantization (signal),
Computational modeling, OWL, Redundancy, Ordered weighted l1, Sharing
BibRef
Zhao, Y.[Yu],
Lee, C.K.[Chung-Kuei],
Differentiable Channel Sparsity Search via Weight Sharing within
Filters,
ICPR22(2012-2018)
IEEE DOI
2212
WWW Link. Image resolution, Shape, Semantic segmentation, Memory management,
Filtering algorithms, Information filters, Stability analysis
BibRef
Humble, R.[Ryan],
Shen, M.[Maying],
Latorre, J.A.[Jorge Albericio],
Darve, E.[Eric],
Alvarez, J.[Jose],
Soft Masking for Cost-Constrained Channel Pruning,
ECCV22(XI:641-657).
Springer DOI
2211
BibRef
Li, Y.[Yawei],
Adamczewski, K.[Kamil],
Li, W.[Wen],
Gu, S.H.[Shu-Hang],
Timofte, R.[Radu],
Van Gool, L.J.[Luc J.],
Revisiting Random Channel Pruning for Neural Network Compression,
CVPR22(191-201)
IEEE DOI
2210
Training, Neural network compression, Benchmark testing,
Network architecture, Filtering algorithms, Machine learning,
retrieval
BibRef
Joo, D.G.[Dong-Gyu],
Kim, D.[Doyeon],
Yi, E.[Eojindl],
Kim, J.[Junmo],
Linear Combination Approximation of Feature for Channel Pruning,
ECV22(2771-2780)
IEEE DOI
2210
Deep learning, Correlation, Convolution,
Neural networks, Linearity
BibRef
Wang, Z.[Zi],
Li, C.C.[Cheng-Cheng],
Channel Pruning via Lookahead Search Guided Reinforcement Learning,
WACV22(3513-3524)
IEEE DOI
2202
Training, Degradation, Monte Carlo methods,
Neural networks, Reinforcement learning, Benchmark testing,
Deep Learning Deep Learning -> Efficient Training and
Inference Methods for Networks
BibRef
Lin, R.[Rui],
Ran, J.[Jie],
Wang, D.P.[Dong-Peng],
Chiu, K.H.[King Hung],
Wong, N.[Ngai],
EZCrop: Energy-Zoned Channels for Robust Output Pruning,
WACV22(3595-3604)
IEEE DOI
2202
Runtime, Codes, Fast Fourier transforms,
Frequency-domain analysis, Robustness, Computational efficiency,
Analysis and Understanding
BibRef
Cho, S.[Sungmin],
Kim, H.[Hyeseong],
Kwon, J.[Junseok],
Filter Pruning Via Softmax Attention,
ICIP21(3507-3511)
IEEE DOI
2201
Image processing, Probabilistic logic,
Softmax attention channel pruning, relative depth-wise separable convolutions
BibRef
Shen, S.B.[Shi-Bo],
Li, R.P.[Rong-Peng],
Zhao, Z.F.[Zhi-Feng],
Zhang, H.G.[Hong-Gang],
Zhou, Y.[Yugeng],
Learning to Prune in Training via Dynamic Channel Propagation,
ICPR21(939-945)
IEEE DOI
2105
Training, Convolutional codes, Heuristic algorithms,
Neural networks, Benchmark testing, Filtering algorithms
BibRef
He, J.J.[Jun-Jie],
Chen, B.[Bohua],
Ding, Y.Z.[Yin-Zhang],
Li, D.X.[Dong-Xiao],
Feature Variance Ratio-guided Channel Pruning for Deep Convolutional
Network Acceleration,
ACCV20(IV:170-186).
Springer DOI
2103
BibRef
Duan, H.R.[Hao-Ran],
Li, H.[Hui],
Channel Pruning for Accelerating Convolutional Neural Networks via
Wasserstein Metric,
ACCV20(III:492-505).
Springer DOI
2103
BibRef
Lee, M.K.,
Lee, S.,
Lee, S.H.,
Song, B.C.,
Channel Pruning Via Gradient Of Mutual Information For Light-Weight
Convolutional Neural Networks,
ICIP20(1751-1755)
IEEE DOI
2011
Mutual information, Probability distribution, Random variables,
Convolutional neural networks, Linear programming, Uncertainty,
mutual information
BibRef
Guo, S.,
Wang, Y.,
Li, Q.,
Yan, J.,
DMCP: Differentiable Markov Channel Pruning for Neural Networks,
CVPR20(1536-1544)
IEEE DOI
2008
Markov processes, Training, Task analysis,
Mathematical model, Learning (artificial intelligence), Optimization
BibRef
Liu, Z.,
Mu, H.,
Zhang, X.,
Guo, Z.,
Yang, X.,
Cheng, K.,
Sun, J.,
MetaPruning: Meta Learning for Automatic Neural Network Channel
Pruning,
ICCV19(3295-3304)
IEEE DOI
2004
Code, Neural Networks.
WWW Link. learning (artificial intelligence), neural nets,
sampling methods, stochastic processes, pruned networks, Task analysis
BibRef
Hu, Y.,
Sun, S.,
Li, J.,
Zhu, J.,
Wang, X.,
Gu, Q.,
Multi-Loss-Aware Channel Pruning of Deep Networks,
ICIP19(889-893)
IEEE DOI
1910
deep neural networks, object classification, model compression, channel pruning
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Net Compression .