Channel Pruning for Accelerating Very Deep Neural Networks

Deep Residual Learning for Image Recognition

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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 classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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Scikit-learn: Machine Learning in Python

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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ImageNet: A large-scale hierarchical image database

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Gradient-based learning applied to document recognition

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You Only Look Once: Unified, Real-Time Object Detection

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Regression shrinkage and selection via the lasso

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ACP: Adaptive Channel Pruning for Efficient Neural Networks

Zhang, Y., Yuan, Y., & Wang, Q. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747839 · Cited by 14

A Multilayer Network-Based Approach to Represent, Explore and Handle Convolutional Neural Networks

Amelio, A., Bonifazi, G., Corradini, E., Domenico Ursino, A., & Virgili, L. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10084-6 · Cited by 14

Attention U-Net Based on Bi-ConvLSTM and Its Optimization for Smart Healthcare

Gao, Y., Yang, L. T., Yang, J., Wang, H., & Zhao, Y. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3237923 · Cited by 14

CALYOLOv4: lightweight YOLOv4 target detection based on coordinated attention

Wang, H., Qian, H., Feng, S., & Yan, S. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05380-3 · Cited by 14

Automated Assessment of Comprehension Strategies from Self-Explanations Using LLMs

Nicula, B., Dascălu, M., Arner, T., Balyan, R., & McNamara, D. S. (2023) · Information · doi:10.3390/info14100567 · Cited by 15

Efficient human activity classification via sparsity‐driven transfer learning

Du, H., Jin, T., Song, Y., Dai, Y., & Li, M. (2019) · IET Radar Sonar & Navigation · doi:10.1049/iet-rsn.2019.0044 · Cited by 14

Mitigating severe over-parameterization in deep convolutional neural networks through forced feature abstraction and compression with an entropy-based heuristic

Gowdra, N., Sinha, R., MacDonell, S. G., & Yan, W. Q. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108057 · Cited by 15

Pack and Detect

Kumar, A. R., Ravindran, B., & Raghunathan, A. (2019) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3297001.3297020 · Cited by 17

Efficient Bitwidth Search for Practical Mixed Precision Neural NetworkPreprint

Li, Y., Wang, W., Bai, H., Gong, R., Dong, X., & Yu, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.07577 · Cited by 14

Neuron Merging: Compensating for Pruned NeuronsPreprint

Kim, W., Kim, S., Park, M.-C., & Jeon, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.13160 · Cited by 14

Good Students Play Big Lottery Better.Preprint

Ma, H., Chen, T., Hu, T.-K., You, C., Xie, X. S., & Wang, Z. (2021) · arXiv (Cornell University) · Cited by 14

Adaptive Discrepancy Masked Distillation for remote sensing object detection

Li, C., Cheng, G., & Han, J. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.02.006 · Cited by 13

Toward Intelligent AIoT: A Comprehensive Survey on Digital Twin and Multimodal Generative AI Integration

Luo, X., Wang, A., Zhang, X., Huang, K., Wang, S., Chen, L., & Cui, Y. (2025) · Mathematics · doi:10.3390/math13213382 · Cited by 19

RTUAV-YOLO: A Family of Efficient and Lightweight Models for Real-Time Object Detection in UAV Aerial Imagery

Zhang, R., Hou, J., Li, L., Zhang, K., Li, Z., & Gao, S. (2025) · Sensors · doi:10.3390/s25216573 · Cited by 13

Artificial Intelligence-Enhanced Wearable Blood Pressure Monitoring in Resource-Limited Settings: A Co-Design of Sensors, Model, and Deployment

Zhang, Y., Qiu, S., Du, K., Wu, S. P., Xiang, T., Zheng, K., Liu, Z., Chen, H., Ji, N., Wang, F., et al. (2026) · Nano-Micro Letters · doi:10.1007/s40820-025-02003-9 · Cited by 18

Pruning Ratio Optimization with Layer-Wise Pruning Method for Accelerating Convolutional Neural Networks

KAMMA, K., INOUE, S., & Wada, T. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7096 · Cited by 13

A Lightweight Automatic Wildlife Recognition Model Design Method Mitigating Shortcut Learning

Zhong, Y., Li, X., Xie, J., & Zhang, J. (2023) · Animals · doi:10.3390/ani13050838 · Cited by 13

Grow-push-prune: Aligning deep discriminants for effective structural network compression

Tian, Q., Arbel, T., & Clark, J. J. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103682 · Cited by 17

Single-Path Bit Sharing for Automatic Loss-Aware Model Compression

Liu, J., Zhuang, B., Chen, P., Shen, C., Cai, J., & Tan, M. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275159 · Cited by 14

CFDP: Common Frequency Domain Pruning

Khaki, S., & Luo, W. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00499 · Cited by 13

Bit-shrinking: Limiting Instantaneous Sharpness for Improving Post-training Quantization

Lin, C., Peng, B., Li, Z., Tan, W., Ren, Y., Xiao, J., & Pu, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01554 · Cited by 13

RASP: Regularization-based Amplitude Saliency Pruning

Zhen, C., Zhang, W., Mo, J., Ji, M., Zhou, H., & Zhu, J. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.09.002 · Cited by 13

Compact Neural Network via Stacking Hybrid Units

Lan, W., Cheung, Y., Jiang, J., Hu, Z., & Li, M. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3323496 · Cited by 15

Mini but Mighty: Finetuning ViTs with Mini Adapters

Marouf, I. E., Tartaglione, E., & Lathuilière, S. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00175 · Cited by 13

Inertia matrix identification of combined spacecraft using a deep neural network with optimized network structure

Wu, S., Chu, W., Wu, Z., Chen, W., & Wang, W. (2023) · Advances in Space Research · doi:10.1016/j.asr.2023.11.024 · Cited by 14

Enhancing Deep Neural Networks through Pruning followed by Quantization Pipeline: A Comprehensive Review

M, M. S., Madnur, P., Dabade, S. H., Khanapure, A., Rodrigues, S., Hegde, S. S., & Kulkarni, U. (2023) · International Conference on Futuristic Technologies (INCOFT) · doi:10.1109/incoft60753.2023.10425695 · Cited by 13

A severity estimation method for lightweight cucumber leaf disease based on DM-BiSeNet

Li, K., Song, Y., Zhu, X., & Zhang, L. (2024) · Information Processing in Agriculture · doi:10.1016/j.inpa.2024.03.003 · Cited by 13

Towards Lightweight Super-Resolution With Dual Regression Learning

Guo, Y., Tan, M., Deng, Z., Wang, J., Chen, Q., Cao, J., Xu, Y., & Chen, J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3406556 · Cited by 21

Efficient filter pruning: Reducing model complexity through redundancy graph decomposition

Li, J., Shao, H., Deng, X., & Jiang, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128108 · Cited by 13

Towards real-time embodied AI agent: a bionic visual encoding framework for mobile robotics

Hou, X.-Y., Guan, Y.-J., Han, T., & Wang, C. (2024) · International Journal of Intelligent Robotics and Applications · doi:10.1007/s41315-024-00363-w · Cited by 14

DSC: Dense-Sparse Convolution for Vectorized Inference of Convolutional Neural Networks

Frickenstein, A., Vemparala, M. R., Unger, C., Ayar, F., & Stechele, W. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00175 · Cited by 13

An Effective Yawn Behavior Detection Method in Classroom

Wang, Z., Jiang, F., & Shen, R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-36708-4_35 · Cited by 13

Convolutional Neural Network Pruning Using Filter Attenuation

Pasandi, M. M., Hajabdollahi, M., Karimi, N., Samavi, S., & Shirani, S. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9191098 · Cited by 14

A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks

Ma, Y., Chen, R., Li, W., Shang, F., Yu, W., Cho, M., & Yu, B. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00060 · Cited by 14

SBNN: Slimming binarized neural network

Wu, Q., Lu, X., Xue, S., Wang, C., Wu, X., & Fan, J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.03.030 · Cited by 14

Deep network compression based on partial least squares

Jordão, A., Yamada, F., & Schwartz, W. R. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.03.108 · Cited by 13

Flexible Group-Level Pruning of Deep Neural Networks for On-Device Machine Learning

Kwang-Bae, L., Kim, H., Lee, H., & Shin, D. (2020) · Design, Automation, and Test in Europe · doi:10.23919/date48585.2020.9116287 · Cited by 13

Human Segmentation Based on Compressed Deep Convolutional Neural Network

Miao, J., Sun, K., Liao, X., Leng, L., & Chu, J. (2020) · IEEE Access · doi:10.1109/access.2020.3023746 · Cited by 13

Robust CNN Compression Framework for Security-Sensitive Embedded Systems

Lee, J., & Lee, S. (2021) · Applied Sciences · doi:10.3390/app11031093 · Cited by 14

ASKs: Convolution with any-shape kernels for efficient neural networks

Liu, G., Zhang, K., & Lv, M. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.039 · Cited by 13

Adversarial Robust Model Compression using In-Train Pruning

Vemparala, M. R., Fasfous, N., Frickenstein, A., Sarkar, S., Zhao, Q., Kuhn, S., Frickenstein, L., Singh, A., Unger, C., Nagaraja, N.-S., et al. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00016 · Cited by 14

Multi-task Pruning via Filter Index Sharing: A Many-Objective Optimization Approach

Cheng, H., Wang, Z., Ma, L., Liu, X., & Wei, Z. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09894-x · Cited by 13

Batch Normalization Tells You Which Filter is Important

Oh, J., Kim, H., Baik, S., Hong, C., & Lee, K. M. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00341 · Cited by 14

Towards efficient network compression via Few-Shot Slimming

He, J., Ding, Y., Zhang, M., & Li, D. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.12.011 · Cited by 13

MOO-DNAS: Efficient Neural Network Design via Differentiable Architecture Search Based on Multi-Objective Optimization

Wei, H., Lee, F., Hu, C., & Chen, Q. (2022) · IEEE Access · doi:10.1109/access.2022.3148323 · Cited by 14

Accelerating Bayesian Neural Networks via Algorithmic and Hardware Optimizations

Fan, H., Ferianc, M., Que, Z., Niu, X., Rodrigues, M. R. D., & Luk, W. W. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3153682 · Cited by 13

VWC-SDK: Convolutional Weight Mapping Using Shifted and Duplicated Kernel With Variable Windows and Channels

Rhe, J., Moon, S., & Ko, J. H. (2022) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2022.3173649 · Cited by 13

CorrNet: pearson correlation based pruning for efficient convolutional neural networks

Kumar, A., Yin, B., Shaikh, A. M., Ali, M., & Wei, W. (2022) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-022-01624-5 · Cited by 13

Accelerating and pruning CNNs for semantic segmentation on FPGA

Mori, P., Vemparala, M. R., Fasfous, N., Mitra, S., Sarkar, S., Frickenstein, A., Frickenstein, L., Helms, D., Nagaraja, N. S., Stechele, W., et al. (2022) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3489517.3530424 · Cited by 13

Compression of deep neural networks: bridging the gap between conventional-based pruning and evolutionary approach

Zhang, Y., Wang, G., Yang, T., Pang, T., He, Z., & Lv, J. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07161-0 · Cited by 13

PDDNet: lightweight congested crowd counting via pyramid depth-wise dilated convolution

Liang, L., Zhao, H., Zhou, F., Ma, M., Yao, F., & Ji, X. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03967-6 · Cited by 13

Filter Clustering for Compressing CNN Model With Better Feature Diversity

Wang, Z., Xie, X., Zhao, Q., & Shi, G. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3216101 · Cited by 14

A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU

Zhao, W., Bai, Y., Sun, Q. L., Li, W., Zheng, H., Jiang, N., Lu, J., Yu, B., & Wong, M. D. F. (2023) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2023.3241110 · Cited by 13

Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis

Lin, J. F., Luo, X., Hong, M., Qu, Y., Xie, Y., & Wu, Z. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01384 · Cited by 13

Beyond Floating-Point Ops: CNN Performance Prediction with Critical Datapath Length

Langerman, D., Johnson, A., Buettner, K., & George, A. D. (2020) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec43674.2020.9286182 · Cited by 13

Identity-linked Group Channel Pruning for Deep Neural Networks

Zhang, C., Xu, K., Liu, J., Zhou, Z., Kang, L., & Ye, D. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533908 · Cited by 13

Transfer channel pruning for compressing deep domain adaptation models

Yu, C., Wang, J., Chen, Y., & Qin, X. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-019-01004-6 · Cited by 13

Shift-based Primitives for Efficient Convolutional Neural NetworksPreprint

Zhong, H., Liu, X., He, Y., & Ma, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.08458 · Cited by 13

Functionality-Oriented Convolutional Filter PruningPreprint

Qin, Z., Yu, F., Liu, C., & Chen, X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.07322 · Cited by 16

Single Shot Structured Pruning Before TrainingPreprint

van Amersfoort, J., Alizadeh, M., Farquhar, S., Lane, N. D., & Gal, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00389 · Cited by 26

Layer Pruning via Fusible Residual Convolutional Block for Deep Neural NetworksPreprint

Xu, P., Cao, J., Shang, F., Sun, W., & Li, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.14356 · Cited by 13

Pufferfish: Communication-efficient Models At No Extra CostPreprint

Wang, H., Agarwal, S., & Papailiopoulos, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03936 · Cited by 17

Adaptive Dynamic Pruning for Non-IID Federated Learning.Preprint

Yu, S., Nguyen, P., Anwar, A., & Jannesari, A. (2021) · arXiv (Cornell University) · Cited by 13

Simplify: A Python library for optimizing pruned neural networks

Bragagnolo, A., & Barbano, C. A. (2021) · SoftwareX · doi:10.1016/j.softx.2021.100907 · Cited by 13

Lightweight remote sensing change detection with progressive multi scale difference aggregation

Fu, Y., Peng, H., Zhao, T., Li, Y., Peng, J., & Zhang, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-10972-5 · Cited by 12

LdsConv: Learned Depthwise Separable Convolutions by Group Pruning

Lin, W., Ding, Y., Wei, H., Pan, X., & Zhang, Y. (2020) · Sensors · doi:10.3390/s20154349 · Cited by 13

FLOPs-efficient filter pruning via transfer scale for neural network acceleration

Guo, Z., Xiao, Y., Liao, W., Veelaert, P., & Philips, W. R. (2021) · Journal of Computational Science · doi:10.1016/j.jocs.2021.101459 · Cited by 12

Compressing Models with Few Samples: Mimicking then Replacing

Wang, H., Liu, J., Ma, X., Yong, Y., Chai, Z., & Wu, J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00078 · Cited by 12

Attention-Aware Learning for Hyperparameter Prediction in Image Processing Pipelines

Qin, H., Han, L., Wang, J., Zhang, C., Li, Y., Li, B., & Hu, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19800-7_16 · Cited by 12

RDJCNN: A micro-convolutional neural network for radar active jamming signal classification

Zhu, H., Guo, S., & Sheng, W. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106417 · Cited by 12

Dense Nested Network Based on Position-Aware Dynamic Parameter Convolution Kernel for Infrared Small Target Detection

Nian, B., Zhang, Y., Zhang, Y., & Shi, H. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3285553 · Cited by 12

Deep learning with ExtendeD Exponential Linear Unit (DELU)

Çatalbaş, B., & Morgül, Ö. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08932-z · Cited by 12

Manipulating Identical Filter Redundancy for Efficient Pruning on Deep and Complicated CNN

Hao, T., Ding, X., Han, J., Guo, Y., & Ding, G. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3298263 · Cited by 22

Decoupled Knowledge Distillation via Spatial Feature Blurring for Hyperspectral Image Classification

Xie, W., Zhang, Z., Jiao, L., & Wang, J. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3383854 · Cited by 12

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

Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency

Mudvari, A., Vainio, A., Ofeidis, I., Tarkoma, S., & Tassiulas, L. (2024) · ACM Transactions on Internet Technology · doi:10.1145/3687471 · Cited by 12

Filter Pruning Based on Information Capacity and Independence

Tang, X., Ye, S., Shi, Y., Hu, T., Peng, Q., & You, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3415068 · Cited by 12

A 28-nm 135.19 TOPS/W Bootstrapped-SRAM Compute-in-Memory Accelerator With Layer-Wise Precision and Sparsity

Mao, W., Liu, D., Zhou, H., Li, F., Li, K., Wu, Q., Yang, J., Cheng, Q., Zhang, L., & Yu, H. (2024) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2024.3486322 · Cited by 12

NeFL: Nested Model Scaling for Federated Learning With System Heterogeneous Clients

Kang, H., Cha, S., Shin, J., Lee, J., & Kang, J. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3549600 · Cited by 22

Adversarial pruning: A survey and benchmark of pruning methods for adversarial robustness

Piras, G., Pintor, M., Demontis, A., Biggio, B., Giacinto, G., & Roli, F. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111788 · Cited by 17

Understanding the Effects of Pre-Training for Object Detectors via Eigenspectrum

Shinya, Y., Simo‐Serra, E., & Suzuki, T. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00242 · Cited by 12

SPEC2: SPECtral SParsE CNN Accelerator on FPGAs

Niu, Y., Zeng, H., Srivastava, A., Lakhotia, K., Kannan, R., Wang, Y., & Prasanna, V. K. (2019) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.1109/hipc.2019.00033 · Cited by 12

GeneCAI

Javaheripi, M., Samragh, M., Javidi, T., & Koushanfar, F. (2020) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3377930.3390226 · Cited by 12

Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation

Shao, R., He, H., Chen, Z., Liu, H., & Liu, D. (2020) · JMIR Formative Research · doi:10.2196/17265 · Cited by 13

Fixed-Sign Binary Neural Network: An Efficient Design of Neural Network for Internet-of-Things Devices

Li, Y., Bao, Y., & Chen, W. (2020) · IEEE Access · doi:10.1109/access.2020.3022902 · Cited by 12

Progressive Kernel Pruning Based on the Information Mapping Sparse Index for CNN Compression

Zhu, J., Zhao, Y., & Pei, J. (2021) · IEEE Access · doi:10.1109/access.2021.3051504 · Cited by 13

Layer-wise Model Pruning based on Mutual Information

Fan, C. C., Li, J., Zhang, T., Ao, X., Wu, F. Q., Meng, Y., & Sun, X. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.246 · Cited by 20

Special Session: Approximate TinyML Systems: Full System Approximations for Extreme Energy-Efficiency in Intelligent Edge Devices

Raha, A., Ghosh, S. K., Mohapatra, D., Mathaikutty, D. A., Sung, R., Brick, C., & Raghunathan, V. (2021) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd53106.2021.00015 · Cited by 11

A Survey on Filter Pruning Techniques for Optimization of Deep Neural Networks

Kulkarni, U., Hallad, S. S., Patil, A., Bhujannavar, T., Kulkarni, S., & Meena, S. M. (2022) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac55078.2022.9987264 · Cited by 12

Deep Learning Architecture Improvement Based on Dynamic Pruning and Layer Fusion

Li, Q., Li, H., & Meng, L. (2023) · Electronics · doi:10.3390/electronics12051208 · Cited by 12

Pruning from Scratch

Wang, Y., Zhang, X., Xie, L., Zhou, J., Su, H., Zhang, B., & Hu, X. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6910 · Cited by 8

SQuantizer: Simultaneous Learning for Both Sparse and Low-precision Neural Networks

Park, M. S., Xu, X., & Brick, C. (2018) · arXiv · doi:10.48550/arxiv.1812.08301 · Cited by 8

An Efficient Object Detection Algorithm Based on Compressed Networks

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