The DOI matches its Crossref record; 63 of the 64 references it cites were found in a registry or an index.
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration
Open accessAbstract
Filter pruning has been widely applied to neural network compression and acceleration. Existing methods usually utilize pre-defined pruning criteria, such as Lp-norm, to prune unimportant filters. There are two major limitations to these methods. First, existing methods fail to consider the variety of filter distribution across layers. To extract features of the coarse level to the fine level, the filters of different layers have various distributions. Therefore, it is not suitable to utilize the same pruning criteria to different functional layers. Second, prevailing layer-by-layer pruning methods process each layer independently and sequentially, failing to consider that all the layers in the network collaboratively make the final prediction. In this paper, we propose Learning Filter Pruning Criteria (LFPC) to solve the above problems. Specifically, we develop a differentiable pruning criteria sampler. This sampler is learnable and optimized by the validation loss of the pruned network obtained from the sampled criteria. In this way, we could adaptively select the appropriate pruning criteria for different functional layers. Besides, when evaluating the sampled criteria, LFPC comprehensively consider the contribution of all the layers at the same time. Experiments validate our approach on three image classification benchmarks. Notably, on ILSVRC-2012, our LFPC reduces more than 60% FLOPs on ResNet-50 with only 0.83% top-5 accuracy loss.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Pruning is the well-studied topic in model compression and many methods have been proposed to advance the network pruning [103].”
From Bringing ai to edge: from deep learning’s perspective (2022) · cites this paper as [103]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
(2017) · Cited by 11,625
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143
Zagoruyko, S., & Komodakis, N. (2016) · Procedings of the British Machine Vision Conference 2016 · doi:10.5244/c.30.87 · Cited by 7,244
Neural Architecture Search with Reinforcement LearningPreprint
Zoph, B., & Le, Q. V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01578 · Cited by 5,705
SGDR: Stochastic Gradient Descent with Warm Restarts
Loshchilov, I., & Hutter, F. (2016) · arXiv · doi:10.48550/arxiv.1608.03983 · Cited by 5,309
Flagged first, then by citations
1 of the 64 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- carreira-perpinán, 0, learning-compression” algorithms for neural net pruning, CVPR
- han, 0, Learning both weights and connections for efficient neural network, NeurIPS
Cited by
Sorted by citations
Items 101–200 of 262
Torque based Structured Pruning for Deep Neural Network
Gupta, A., Bau, T. C., Kim, J., Zhu, Z., Jha, S., & Garud, H. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00269 · Cited by 12
Soft independence guided filter pruning
Liu, Y., Gu, S., Shen, C., Zhao, X.-L., & Hu, Q. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110488 · Cited by 13
Koo, K., & Kim, H. (2023) · IEEE Access · doi:10.1109/access.2023.3326534 · Cited by 12
Joint Pruning & Quantization for Extremely Sparse Neural NetworksPreprint
Yu, P.-H., Wu, S.-S., Klopp, J. P., Chen, L., & Chien, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01892 · Cited by 12
Zhang, M., Feng, J., Yang, H., Guo, J., Li, Y., & Gao, X. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3594958 · Cited by 13
Semantic-Driven Automatic Filter Pruning for Neural Networks
Guo, Y., & Gao, W. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859932 · Cited by 11
Real‐time seed sorting system via 2D information entropy‐based CNN pruning and TensorRt acceleration
Li, C., Li, H., Liao, L., Liu, Z., & Dong, Y. X. (2023) · IET Image Processing · doi:10.1049/ipr2.12747 · Cited by 11
PDAS: Improving network pruning based on Progressive Differentiable Architecture Search for DNNs
Jiang, W., Chen, Y., Wen, S., Zheng, L., & Jin, H. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.04.011 · Cited by 10
Three-Stage Global Channel Pruning for Resources-Limited Platform
Chen, Y., Li, R., Li, W., Wang, J., & Li, R. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3292152 · Cited by 12
Si, Q., & Han, S. I. (2025) · IEEE Access · doi:10.1109/access.2025.3544631 · Cited by 11
Goal driven network pruning for object recognition
Kaplan, C., & Bülbül, A. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107468 · Cited by 12
Chun, D., Choi, J.-W., Lee, H., & Kim, H. (2023) · IEEE Access · doi:10.1109/access.2023.3280552 · Cited by 13
Blending Pruning Criteria for Convolutional Neural Networks
He, W. P., Huang, Z., Liang, M., Liang, S., & Yang, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86380-7_1 · Cited by 12
Compact Model Training by Low-Rank Projection With Energy Transfer
Guo, K., Lin, Z., Chen, C., Xing, X., Liu, F., & Xu, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3400928 · Cited by 10
Linear Combination Approximation of Feature for Channel Pruning
Joo, D., Kim, D., Yi, E., & Kim, J. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00313 · Cited by 10
Searching for Network Width With Bilaterally Coupled Network
Su, X. Q., You, S., Xie, J., Wang, F., Qian, C., Zhang, C., & Xu, C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3226777 · Cited by 10
Ensemble Knowledge Guided Sub-network Search and Fine-Tuning for Filter Pruning
Lee, S., & Song, B. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20083-0_34 · Cited by 10
Weight-adaptive channel pruning for CNNs based on closeness-centrality modeling
Zhao, D., Duan, Y., Zhou, Y., Duan, S., & Hu, X. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05164-5 · Cited by 10
Convolution-Weight-Distribution Assumption: Rethinking the Criteria of Channel PruningPreprint
Huang, Z., Shao, W., Wang, X., Lin, L., & Luo, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.11627 · Cited by 20
Network Pruning via Feature Shift Minimization
Duan, Y., Zhou, Y., He, P., Liu, Q., Duan, S., & Hu, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26319-4_37 · Cited by 9
Li, Q., Li, H., & Meng, L. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01496-8 · Cited by 9
A Novel Deep-Learning Model Compression Based on Filter-Stripe Group Pruning and Its IoT Application
Zhao, M., Tong, X., Wu, W., Wang, Z., Zhou, B., & Huang, X. (2022) · Sensors · doi:10.3390/s22155623 · Cited by 9
Cheng, H., Wang, Z., Ma, L., Wei, Z., Alsaadi, F. E., & Liu, X. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01022-6 · Cited by 9
Lottery 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
Sharma, P. K., Abraham, A., & Rajendiran, V. N. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3134158 · Cited by 8
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
Pruning-and-distillation: One-stage joint compression framework for CNNs via clustering
Niu, T., Teng, Y., Jin, L., Zou, P., & Liu, Y. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104743 · Cited by 9
Evolving filter criteria for randomly initialized network pruning in image classification
Chen, X., Liu, C., Peng, H., Lin, J., Gong, Y., Chen, Y., Peng, D., & Geng, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127872 · Cited by 8
Cheng, H., Zhang, M., & Shi, Q. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3416316 · Cited by 11
Fully shared convolutional neural networks
Lu, Y., Lu, G., Li, J., Zhang, Z., & Xu, Y. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05618-8 · Cited by 8
Global Channel Pruning With Self-Supervised Mask Learning
Ma, M., Zhang, T., Wang, Z., Wang, Y., Du, T., & Li, W. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3488098 · Cited by 7
Compressing Deep Neural Networks with Goal-Specific Pruning and Self-Distillation
Chen, F.-Y., Hsu, Y.-J., Lu, C.-H., Shuai, H.-H., Yeh, L., & Shen, C.-Y. (2025) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3721293 · Cited by 9
Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution
Wang, J., Wang, H., Zhang, Y., Fu, Y., & Tao, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01157 · 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
TAILOR: InTer-feAture distinctIon fiLter fusiOn pRuning
Han, X., Chu, L., Wang, K., Wang, L., Yue, L., Ding, W., Gu, T., & Weng, J. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120229 · Cited by 6
Improving the Robustness of Model Compression by On-Manifold Adversarial Training
Kwon, J., & Lee, S. (2021) · Future Internet · doi:10.3390/fi13120300 · Cited by 6
Automated Filter Pruning Based on High-Dimensional Bayesian Optimization
Kim, T., Choi, H., & Choe, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3153025 · Cited by 7
Convolutional neural network simplification with progressive retraining
Osaku, D., Gomes, J. F., & Falcão, A. X. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.06.032 · Cited by 7
SEArch: A self-evolving framework for network architecture optimization
Liang, Y., Xiang, D., & Li, X. S. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130980 · Cited by 5
Kumar, A., Shibu, A., & Lee, D.-G. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113521 · Cited by 6
Empirical evaluation of filter pruning methods for acceleration of convolutional neural network
Kumar, D., Mehta, M. A., Joshi, V. C., Oza, R. S., Kotecha, K. V., & Lin, J. C. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17656-0 · Cited by 5
XiaoFeng, R. U. A. N., Hu, W., Liu, Y., & Li, B. (2021) · Scientia Sinica Technologica · doi:10.1360/sst-2021-0088 · Cited by 5
AntiDoteX: Attention-Based Dynamic Optimization for Neural Network Runtime Efficiency
Yu, F., Xu, Z., Liu, C., Stamoulis, D. S., Wang, D., Wang, Y., & Chen, X. (2022) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2022.3144616 · Cited by 5
PSE-Net: Channel pruning for Convolutional Neural Networks with parallel-subnets estimator
Wang, S., Xie, T., Liu, H., Zhang, X., & Cheng, J. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106263 · Cited by 5
Efficient Bayesian CNN Model Compression using Bayes by Backprop and L1-Norm Regularization
Shaikh, A. M., Zhao, Y., Kumar, A., Ali, M., & Kang, Y. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11593-1 · Cited by 5
APSSF: Adaptive CNN Pruning Based on Structural Similarity of Filters
Geng, L., & Niu, B. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00518-4 · Cited by 5
GAP: A group-based automatic pruning algorithm via convolution kernel fusion
Chen, D., Lin, K., & Deng, Q. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128488 · Cited by 5
Pruning the Unimportant or Redundant Filters? Synergy Makes Better
Cai, Y., Yin, Z., Guo, K., & Xu, X. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533406 · Cited by 5
Chen, L., Gong, S., Shi, X., & Shang, M. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.760554 · Cited by 5
Error Resilient In-Memory Computing Architecture for CNN Inference on the Edge
Rios, M. A., Ponzina, F., Ansaloni, G., Levisse, A., & Atienza, D. (2022) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3526241.3530351 · Cited by 5
Zhang, H., Liu, L., Kang, B., & Zheng, N. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3216477 · Cited by 5
Self-citationCausal Unstructured Pruning in Linear Networks Using Effective Information
Zeng, C., Liu, L., Zhao, H., Zhang, Y., Wang, W., Cai, N., & Yue, Y. (2022) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc55534.2022.00056 · Cited by 5
Optg: Optimizing Gradient-Driven Criteria in Network SparsityPreprint
Zhang, Y., Lin, M., Chen, M., Chao, F., Tian, Y., & Ji, R. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4829145 · Cited by 13
Wen, N., Zhou, Y. C., Wang, Y., Zheng, Y., Fan, Y., Liu, Y., Wang, Y., & Li, M. (2025) · Sensors · doi:10.3390/s25072089 · Cited by 4
GCPNet: Gradient-aware channel pruning network with bilateral coupled sampling strategy
Zhang, Z., Cao, C., Zheng, F., Sun, T., & Zhao, L.-J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126104 · Cited by 4
Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks
Fracastoro, G., Fosson, S. M., Migliorati, A., & Calafiore, G. C. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3373609 · Cited by 4
Automatic Channel Pruning with Hyper-parameter Search and Dynamic Masking
Li, B., Fan, Y., Pan, Z., Bian, Y., & Zhang, G. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475370 · Cited by 4
Wu, R., Liu, H., & Li, J. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103516 · Cited by 4
AdaptCL: Efficient Collaborative Learning with Dynamic and Adaptive PruningPreprint
Zhou, G., Xu, K., Li, Q. L., Liu, Y., & Zhao, Y. X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.14126 · Cited by 4
Automated Deepfake DetectionPreprint
Liu, P., Lin, Y., He, Y., Wei, Y., Zhen, L., Zhou, J. T., Goh, R. S. M., & Liu, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.10705 · Cited by 11
Self-citationManipulating Identical Filter Redundancy for Efficient Pruning on Deep and Complicated CNNPreprint
Ding, X., Hao, T., Han, J., Guo, Y., & Ding, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.14444 · Cited by 4
MaintnDist: Filter pruning via maintaining feature distribution
Chu, Y., Han, X., Wang, L., & Zhang, C. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114009 · Cited by 3
MLPruner: pruning convolutional neural networks with automatic mask learning
Chen, S., & Zhao, Y. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3132 · Cited by 4
Compressing Vision Transformer from the View of Model Property in Frequency Domain
Wang, Z., Xie, X., Luo, H., Huang, T., Dong, W., Xiong, K., Liu, Y., Li, X., Wang, F., & Shi, G. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02561-w · Cited by 3
Iterative multi-criteria filter pruning for efficient convolutional neural network deployment
Bellebna, M. E.-A., & Berrani, S.-A. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08317-8 · Cited by 3
Pei, J., Huang, Z., & Zhu, J. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18615-z · Cited by 3
Tailored Channel Pruning: Achieve Targeted Model Complexity Through Adaptive Sparsity Regularization
Lee, S., Jeon, Y., Lee, S., & Kim, J. (2025) · IEEE Access · doi:10.1109/access.2025.3529465 · Cited by 3
PrivCore: Multiplication-activation co-reduction for efficient private inference
Pang, Z., Wang, L., Yu, F., Zhao, K., Zeng, B., & Xu, S. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107307 · Cited by 3
A Pruning Method Based on the Dissimilarity of Angle Among Channels and Filters
Yao, J., Li, P., Kang, X., & Wang, Y. (2022) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai56018.2022.00084 · Cited by 3
Accelerating distributed machine learning with model compression and graph partition
Duan, Y., & Wu, J. (2023) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2023.04.006 · Cited by 3
Rewarded Meta-Pruning: Meta Learning with Rewards for Channel Pruning
Shibu, A., Kumar, A., Jung, H., & Lee, D.-G. (2023) · Mathematics · doi:10.3390/math11234849 · Cited by 3
Building efficient CNNs using Depthwise Convolutional Eigen-Filters (DeCEF)
Yu, Y., Scheidegger, S., & McKelvey, T. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128461 · Cited by 3
Chen, Q., Diao, B., Yang, Y., & Xu, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78186-5_16 · Cited by 3
Self-citationDeep Convolutional Neural Networks Structured Pruning via Gravity Regularization
Ferdi, A. (2024) · IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) · doi:10.1109/icmlant63295.2024.00009 · Cited by 3
Knowledge distilled pre-training model for vision-language-navigation
Huang, B., Zhang, S., Huang, J. T., Yu, Y., Shi, Z., & Xiong, Y.-J. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03779-8 · Cited by 5
ACP: Automatic Channel Pruning via Clustering and Swarm Intelligence Optimization for CNNPreprint
Chang, J., Lu, Y., Xue, P., Xu, Y., & Wei, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.06407 · Cited by 3
Automatic Neural Network Pruning that Efficiently Preserves the Model AccuracyPreprint
Castells, T., & Yeom, S.-K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.09635 · Cited by 5
FreeNet: An efficient frequency-domain early exiting network for dynamic inference
Yan, Y., Li, X., Cui, K., Sun, H., & Yu, Z. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113155 · Cited by 3
CLAD: Criterion learner and attention distillation for automated CNN prunning
Li, Z., Li, J., Liu, S., Zhao, B., & Liu, D. (2025) · Journal of Automation and Intelligence · doi:10.1016/j.jai.2025.08.002 · Cited by 2
yuan, T., Zhang, X., Zhang, Q., & Tan, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-42769-5 · Cited by 2
Filter Pruning Via Filters Similarity in Consecutive Layers
Wang, X., Wang, J., Tang, X., Gao, P., Fang, R., & Xie, G. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096721 · Cited by 2
RepSGD: Channel Pruning Using Reparamerization for Accelerating Convolutional Neural Networks
Kim, N. J., & Kim, H. (2023) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas46773.2023.10181631 · Cited by 2
Neural Network Light Weighting Approach Using Multi-Metric Evaluation of Convolution Kernels
Wang, P., Ren, T., Liu, J., Meng, F., Liu, X., & Liu, W. (2023) · IEEE Access · doi:10.1109/access.2023.3305984 · Cited by 3
Feature independent Filter Pruning by Successive Layers analysis
Mondal, M., Das, B., Lall, B., Singh, P., Roy, S. D., & Joshi, S. D. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103828 · Cited by 2
Singh, D., Rao, T. P., Veeranjaneyulu, N., & Reddy, T. S. K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19167-y · Cited by 2
Differentiable Network Pruning via Polarization of Probabilistic Channelwise Soft Masks
Ma, M., Wang, J., & Yu, Z. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7775419 · Cited by 2
Tang, Z., Li, Z., Yang, J., & Qi, F. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-10813-w · Cited by 2
Accurate and Efficient Channel pruning via Orthogonal Matching Pursuit
Purohit, K., Parvathgari, A. R., Das, S., & Bhattacharya, S. (2022) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1145/3564121.3564139 · Cited by 2
Towards a Lightweight Object Detection through Model Pruning Approaches
Yu, H., Lee, S., Yeo, B., Han, J., Park, E., & Pack, S. (2023) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc58733.2023.10393543 · Cited by 2
Students and teachers learning together: a robust training strategy for neural network pruning
Xiong, L., Chen, Q., Huang, J., Huang, X., Huang, P. C., & Wei, S. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01315-x · Cited by 2
Deep discriminant analysis for task-dependent compact network searchPreprint
Tian, Q., Arbel, T., & Clark, J. J. (2020) · arXiv (Cornell University) · Cited by 2
Lottery Ticket Implies Accuracy Degradation, Is It a Desirable Phenomenon?Preprint
Liu, N., Yuan, G., Che, Z., Shen, X., Ma, X., Jin, Q., Ren, J., Tang, J., Liu, S., & Wang, Y. (2021) · arXiv (Cornell University) · Cited by 2
Mana, S. C., Rajesh, S., Kalaiarasi, G., Chandrasekaran, H., & Kanipriya, M. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09037-3 · Cited by 1
A Systematic Overview of Meta-pruning Strategies in Deep Learning
Patilkulkarni, D., Shetty, S., Kulkarni, P., Hanchinamani, S., Kulkarni, S., & Kulkarni, U. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-2004-0_40 · Cited by 1
POEM: Pruning with Output Error Minimization for Compressing Deep Neural Networks
KAMMA, K., & Wada, T. (2024) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2023edp7165 · Cited by 1
Ambuj (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129901 · Cited by 1
Cai, J., Lian, S., Zhao, Y., Zhu, J., & Pei, J. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105161 · Cited by 1
Advancing Weight and Channel Sparsification with Enhanced Saliency
Sun, X., Shen, M., Yin, H., Mao, L., Molchanov, P. A., & Álvarez, J. M. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00704 · Cited by 1
Efficient CNNs via Passive Filter Pruning
Singh, A., & Plumbley, M. D. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3561589 · Cited by 1
Metadata
- Authors
- He, Y., Ding, Y., Liu, P., Zhu, L., Zhang, H., & Yang, Y.
- Affiliations
- University of Technology Sydney (AU)
- Baidu (China) (CN)
- Nanyang Technological University (SG)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 2006-2015
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035467254
- Cited by
- 262
- References
- 64
- Citations by year
- 2026: 9 · 2025: 35 · 2024: 43 · 2023: 62 · 2022: 55 · 2021: 49 · 2020: 9
- FWCI
- 13.50
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- 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)
- OPUS - Open Publications of UTS Scholars (University of Technology Sydney) (submittedVersion)
- 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
- filter pruning, neural network compression, deep convolutional neural networks, layer-wise pruning, model acceleration, FLOPs reduction, image classification, ResNet50
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
