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

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ImageNet classification with deep convolutional neural networks

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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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Differentiable Subset Pruning of Transformer Heads

Li, J., Cotterell, R., & Sachan, M. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00436 · Cited by 49

Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A SurveyPreprint

Liu, J., Tripathi, S., Kurup, U., & Shah, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.04275 · Cited by 41

EdgeViT: Efficient Visual Modeling for Edge Computing

Chen, Z., Zhong, F., Luo, Q., Zhang, X., & Zheng, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19211-1_33 · Cited by 44

HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity

Wu, Y. N., Tsai, P.-A., Muralidharan, S., Parashar, A., Sze, V., & Emer, J. (2023) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3613424.3623786 · Cited by 47

Efficient and accurate compound scaling for convolutional neural networks

Lin, C.-M., Yang, P., Wang, Q., Qiu, Z., Lv, W., & Wang, Z. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.08.053 · Cited by 46

Towards Evolutionary Compression

Wang, Y., Xu, C., Qiu, J., Xu, C., & Tao, D. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219970 · Cited by 54

Distilling Channels for Efficient Deep Tracking

Ge, S., Luo, Z., Zhang, C., Hua, Y., & Tao, D. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2950508 · Cited by 39

HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions

Li, D., Zhou, A., & Yao, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00341 · Cited by 40

Dynamic Network Pruning with Interpretable Layerwise Channel Selection

Wang, Y., Zhang, X., Hu, X., Zhang, B., & Su, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6098 · Cited by 40

Differential Evolution Based Layer-Wise Weight Pruning for Compressing Deep Neural Networks

Wu, T., Li, X., Zhou, D., Li, N., & Shi, J. (2021) · Sensors · doi:10.3390/s21030880 · Cited by 41

Deep neural network compression through interpretability-based filter pruning

Yao, K., Cao, F., Leung, Y., & Liang, J. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108056 · Cited by 40

Edge Devices for Internet of Medical Things: Technologies, Techniques, and Implementation

Dhaou, I. B., Ebrahimi, M., Ammar, M. B., Bouattour, G., & Kanoun, O. (2021) · Electronics · doi:10.3390/electronics10172104 · Cited by 40

TinyPillarNet: Tiny Pillar-Based Network for 3D Point Cloud Object Detection at Edge

Li, Y., Zhang, Y., & Lai, R. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3297620 · Cited by 39

Trapezoid: A Versatile Accelerator for Dense and Sparse Matrix Multiplications

Yang, Y., Emer, J., & Sánchez, D. (2024) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca59077.2024.00072 · Cited by 41

LIT: Learned Intermediate Representation Training for Model Compression

Koratana, A., Kang, D. D., Bailis, P. D., & Zaharia, M. (2019) · International Conference on Machine Learning · Cited by 39

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

Radu, V., Kaszyk, K., Wen, Y., Turner, J., Cano, J., Crowley, E. J., Franke, B., Storkey, A., & O’boyle, M. F. P. (2019) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc47752.2019.9042000 · Cited by 39

Deep Neural Network Self-Distillation Exploiting Data Representation Invariance

Xu, T.-B., & Liu, C. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027634 · Cited by 39

Document image classification: Progress over two decades

Liu, L., Wang, Z., Qiu, T., Chen, Q., Lu, Y., & Suen, C. Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.114 · Cited by 40

Effective Training of Convolutional Neural Networks With Low-Bitwidth Weights and Activations

Zhuang, B., Tan, M., Liu, J., Liu, L., Reid, I. D., & Shen, C. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3088904 · Cited by 42

MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing

Li, Y., Zhu, F., Gong, R., Shen, M., Dong, X., Yu, F., Lu, S., & Gu, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00437 · Cited by 43

Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization

Li, Y., Zhao, P., Yuan, G., Lin, X., Wang, Y., & Chen, X. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/449 · Cited by 43

Layer-compensated Pruning for Resource-constrained Convolutional Neural NetworksPreprint

Chin, T.-W., Zhang, C., & Marculescu, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.00518 · Cited by 40

ChipNet: Budget-Aware Pruning with Heaviside Continuous ApproximationsPreprint

Tiwari, R., Bamba, U., Chavan, A., & Gupta, D. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.07156 · Cited by 45

Faster and accurate green pepper detection using NSGA-II-based pruned YOLOv5l in the field environment

Nan, Y., Zhang, H., Zeng, Y., Zheng, J., & Ge, Y. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107563 · Cited by 38

Distributed Machine Learning in Edge Computing: Challenges, Solutions and Future Directions

Tu, J., Yang, L., & Cao, J. (2024) · ACM Computing Surveys · doi:10.1145/3708495 · Cited by 48

Compressing CNN-DBLSTM models for OCR with teacher-student learning and Tucker decomposition

Ding, H., Chen, K., & Huo, Q. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.07.002 · Cited by 38

A dynamic CNN pruning method based on matrix similarity

Shao, M., Dai, J., Kuang, J., & Meng, D. (2020) · Signal Image and Video Processing · doi:10.1007/s11760-020-01760-x · Cited by 38

3D CNN Acceleration on FPGA using Hardware-Aware Pruning

Sun, M., Zhao, P., Gungor, M., Pedram, M., Leeser, M. E., & Lin, X. (2020) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18072.2020.9218571 · Cited by 38

Accelerating CNN Training by Pruning Activation Gradients

Ye, X., Dai, P., Luo, J., Guo, X., Qi, Y., Yang, J., & Chen, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58595-2_20 · Cited by 39

Learning Efficient Binarized Object Detectors With Information Compression

Wang, Z., Lu, J., Wu, Z., & Zhou, J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3050464 · Cited by 38

Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI

Yuan, G., Liao, Z., Ma, X., Cai, Y., Kong, Z., Shen, X., Fu, J., Li, Z., Zhang, C., Peng, H., et al. (2021) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed51717.2021.9424332 · Cited by 39

JointPruning: Pruning Networks Along Multiple Dimensions for Efficient Point Cloud Processing

Guo, J., Liu, J., & Xu, D. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3105820 · Cited by 38

GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization

Guo, Y., Yuan, H., Tan, J., Wang, Z., Yang, S., & Liu, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00519 · Cited by 42

FPC: Filter pruning via the contribution of output feature map for deep convolutional neural networks acceleration

Chen, Y., Wen, X., Zhang, Y., & He, Q. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107876 · Cited by 39

Differentiable neural architecture learning for efficient neural networks

Guo, Q., Wu, X., Kittler, J., & Feng, Z. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2021.108448 · Cited by 41

Structured Sparsity Learning for Efficient Video Super-Resolution

Xia, B., He, J., Zhang, Y., Wang, Y., Tian, Y., Yang, W., & Gool, L. V. (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/cvpr52729.2023.02168 · Cited by 38

Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMMPreprint

Ye, S., Feng, X., Zhang, T., Ma, X., Lin, S., Li, Z., Xu, K., Wen, W., Liu, S., Tang, J., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.09769 · Cited by 38

ResPrune: An energy-efficient restorative filter pruning method using stochastic optimization for accelerating CNN

Jayasimhan, A., & Pabitha, P. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110671 · Cited by 38

Model Parallelism Optimization for Distributed Inference via Decoupled CNN Structure

Du, J., Zhu, X., Shen, M., Du, Y., Lu, Y., Xiao, N., & Liao, X. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3041474 · Cited by 41

Pruning by Training: A Novel Deep Neural Network Compression Framework for Image Processing

Tian, G., Chen, J., Zeng, X., & Liu, Y. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3054315 · Cited by 37

BCNet: Searching for Network Width with Bilaterally Coupled Network

Su, X. Q., You, S., Wang, F., Qian, C., Zhang, C., & Xu, C. (2021) · 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/cvpr46437.2021.00221 · Cited by 37

NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration

Li, Z., Yuan, G., Niu, W., Zhao, P., Li, Y., Cai, Y., Shen, X., Zhan, Z., Kong, Z., Jin, Q., et al. (2021) · 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/cvpr46437.2021.01403 · Cited by 40

Two-Stage Evolutionary Neural Architecture Search for Transfer Learning

Wen, Y.-W., Peng, S.-H., & Ting, C.-K. (2021) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2021.3097937 · Cited by 36

Anomaly Detection using Distributed Log Data: A Lightweight Federated Learning Approach

Guo, Y., Wu, Y., Zhu, Y., Yang, B., & Han, C. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533294 · Cited by 37

Dynamic Sparse Graph for Efficient Deep LearningPreprint

Liu, L., Deng, L., Hu, X., Zhu, M., Li, G., Ding, Y., & Xie, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.00859 · Cited by 43

Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization

Balaskas, K., Καρατζάς, Α., Sad, C., Siozios, K., Anagnostopoulos, I., Zervakis, G., & Henkel, J. (2024) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2023.3346944 · Cited by 43

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Turner, J., Cano, J., Radu, V., Crowley, E. J., O’boyle, M. F. P., & Storkey, A. (2018) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc.2018.8573503 · Cited by 37

Structured Pruning of Convolutional Neural Networks via L1 Regularization

Yang, C., Yang, Z., Khattak, A. M., Liu, Y., Zhang, W., Gao, W., & Wang, M. (2019) · IEEE Access · doi:10.1109/access.2019.2933032 · Cited by 38

Scalable Person Re-Identification by Harmonious Attention

Li, W., Zhu, X., & Gong, S. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01274-1 · Cited by 35

Learning Channel-Wise Interactions for Binary Convolutional Neural Networks

Wang, Z., Lu, J., & Zhou, J. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2988262 · Cited by 37

Dynamic slicing for deep neural networks

Zhang, Z., Li, Y., Guo, Y., Chen, X., & Liu, Y. (2020) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3368089.3409676 · Cited by 35

Improving the Performance of VGG Through Different Granularity Feature Combinations

Zhou, Y., Chang, H., Lu, Y., Lu, X., & Zhou, R. (2020) · IEEE Access · doi:10.1109/access.2020.3031908 · Cited by 38

Deep neural network compression by Tucker decomposition with nonlinear response

Liu, Y., & Ng, M. K. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108171 · Cited by 36

Representation and compression of Residual Neural Networks through a multilayer network based approach

Amelio, A., Bonifazi, G., Cauteruccio, F., Corradini, E., Marchetti, M., Domenico Ursino, A., & Virgili, L. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119391 · Cited by 36

A lightweight model combining convolutional neural network and Transformer for driver distraction recognition

Tang, X., Chen, Y., Ma, Y., Yang, W., Zhou, H., & Huang, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107910 · Cited by 37

Recurrent Residual Module for Fast Inference in Videos

Pan, B., Lin, W., Fang, X., Huang, C., Zhou, B., & Lu, C. (2018) · 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/cvpr.2018.00166 · Cited by 34

Complexity-Scalable Neural-Network-Based MIMO Detection With Learnable Weight Scaling

Mohammad, A., Masouros, C., & Andreopoulos, Y. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.3007622 · Cited by 35

PreVIous: A Methodology for Prediction of Visual Inference Performance on IoT Devices

Velasco-Montero, D., Fernández‐Berni, J., Carmona-Galan, R., & Rodriguez-Vazquez, A. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2981684 · Cited by 37

Filter Distillation for Network Compression

Suau, X., Zappella, L., & Apostoloff, N. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093546 · Cited by 56

Depthwise Nonlocal Module for Fast Salient Object Detection Using a Single Thread

Li, H., Li, G., Yang, B., Chen, G., Lin, L., & Yu, Y. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2969282 · Cited by 35

DataMix: Efficient Privacy-Preserving Edge-Cloud Inference

Liu, Z., Wu, Z., Gan, C., Zhu, L., & Han, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58621-8_34 · Cited by 35

Learn-Prune-Share for Lifelong Learning

Wang, Z., Jian, T., Chowdhury, K. R., Wang, Y., Dy, J., & Ioannidis, S. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00073 · Cited by 35

CEModule: A Computation Efficient Module for Lightweight Convolutional Neural Networks

Liang, Y., Li, M., Jiang, C., & Liu, G. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3133127 · Cited by 36

FairGRAPE: Fairness-Aware GRAdient Pruning mEthod for Face Attribute Classification

Lin, X., Kim, S., & Joo, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_24 · Cited by 42

Efficient neural networks for edge devices

Liu, S., Ha, D. S., Shen, F., & Yi, Y. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107121 · Cited by 40

NICE: Noise Injection and Clamping Estimation for Neural Network Quantization

Baskin, C., Zheltonozhkii, E., Rozen, T., Liss, N., Chai, Y., Schwartz, E., Giryes, R., Bronstein, A. M., & Mendelson, A. (2021) · Mathematics · doi:10.3390/math9172144 · Cited by 43

Studying the plasticity in deep convolutional neural networks using random pruning

Mittal, D., Bhardwaj, S., Khapra, M. M., & Ravindran, B. (2019) · Machine Vision and Applications · doi:10.1007/s00138-018-01001-9 · Cited by 34

Multiobjective ResNet pruning by means of EMOAs for remote sensing scene classification

Liu, X., Zhou, Y. Z., Zhao, J., Yao, R., Liu, B., Ma, D., & Zheng, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.11.097 · Cited by 34

Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning

Jung, I., You, K., Noh, H., Cho, M., & Han, B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6779 · Cited by 35

Automatic Generation of Multi-Precision Multi-Arithmetic CNN Accelerators for FPGAs

Zhao, Y., Gao, X., Guo, X., Liu, J., Wang, E., Mullins, R., Cheung, P. Y. K., Constantinides, G. A., & Xu, C. (2019) · International Conference on Field-Programmable Technology (FPT) · doi:10.1109/icfpt47387.2019.00014 · Cited by 37

Pruning Convolutional Neural Networks with an Attention Mechanism for Remote Sensing Image Classification

Zhang, S., Wu, G., Gu, J., & Han, J. (2020) · Electronics · doi:10.3390/electronics9081209 · Cited by 37

Filter Pruning via Learned Representation Median in the Frequency Domain

Zhang, X., Xie, W., Li, Y., Lei, J., & Du, Q. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3124284 · Cited by 34

NAP: Neural architecture search with pruning

Ding, Y., Wu, Y., Huang, C., Tang, S., Wu, F. Q., Yang, Y., Zhu, W., & Zhuang, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.12.002 · Cited by 36

High-efficiency tea shoot detection method via a compressed deep learning model

Li, Y., He, L., Jia, J., Chen, J., Lyu, J., & Wu, C. (2022) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20221503.6896 · Cited by 34

Brain-Inspired Interpretable Network Pruning for Smart Vision-Based Defect Detection Equipment

Wang, J., Zhao, S., Xu, C., Zhang, J., & Zhong, R. Y. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3188349 · Cited by 34

Progressive Weight Pruning of Deep Neural Networks using ADMMPreprint

Ye, S., Zhang, T., Zhang, K., Li, J., Xu, K., Yang, Y., Yu, F., Tang, J., Fardad, M., Liu, S., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.07378 · Cited by 50

Zero Time Waste: Recycling Predictions in Early Exit Neural NetworksPreprint

Wołczyk, M., Wójcik, B., Bałazy, K., Podolak, I. T., Tabor, J., Śmieja, M., & Trzcinski, T. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05409 · Cited by 34

TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device LearningPreprint

Cai, H., Gan, C., Zhu, L., & Han, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.11622 · Cited by 79

FPWT: Filter pruning via wavelet transform for CNNs

Liu, Y., Fan, K., & Zhou, W. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106577 · Cited by 33

Hardware-Aware Softmax Approximation for Deep Neural Networks

Geng, X., Lin, J., Zhao, B., Kong, A., Aly, M. M. S., & Chandrasekhar, V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20870-7_7 · Cited by 34

AddressNet: Shift-Based Primitives for Efficient Convolutional Neural Networks

He, Y., Liu, X., Zhong, H., & Ma, Y. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00134 · Cited by 33

Feature selection based convolutional neural network pruning and its application in calibration modeling for NIR spectroscopy

Chen, Y., & Wang, Z. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.06.004 · Cited by 34

Full-stack optimization for accelerating CNNs using powers-of-two weights with FPGA validation

McDanel, B., Zhang, S. Q., Kung, H. T., & Dong, X. (2019) · International Conference on Supercomputing · doi:10.1145/3330345.3330385 · Cited by 35

Structured Pruning for Efficient Convolutional Neural Networks via Incremental Regularization

Wang, H., Hu, X., Zhang, Q., Wang, Y., Yu, L., & Hu, H. R. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2961233 · Cited by 33

Trained Rank Pruning for Efficient Deep Neural Networks

Xu, Y., Li, Y., Zhang, S., Wen, W., Wang, B., Dai, W., Qi, Y., Chen, Y., Lin, W., & Xiong, H. (2019) · Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) · doi:10.1109/emc2-nips53020.2019.00011 · Cited by 42

Accelerating Neural ODEs Using Model Order Reduction

Lehtimäki, M., Paunonen, L., & Linne, M. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3175757 · Cited by 33

A novel genetic algorithm-based approach for compression and acceleration of deep learning convolution neural network: an application in computer tomography lung cancer data

Sanagala, S. S., Agarwal, M., Utkarsh, K., Gupta, S. K., Koppula, V. K., & Suri, J. S. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07567-w · Cited by 34

Efficient Layer Compression Without Pruning

Wu, J., Zhu, D., Fang, L., Deng, Y. C., & Zhong, Z. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3302519 · Cited by 34

Cross-Camera Inference on the Constrained Edge

Li, J., Liu, L., Xu, H. L., Wu, S., & Xue, C. J. (2023) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom53939.2023.10229045 · Cited by 34

LST-Net: Learning a Convolutional Neural Network with a Learnable Sparse Transform

Li, L., Wang, K., Li, S., Feng, X., & Zhang, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58607-2_33 · Cited by 32

Cascaded Projection: End-To-End Network Compression and Acceleration

Minnehan, B., & Savakis, A. E. (2019) · 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/cvpr.2019.01097 · Cited by 33

AutoGrow: Automatic Layer Growing in Deep Convolutional Networks

Wen, W., Yan, F., Chen, Y., & Li, H. H. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403126 · Cited by 32

AdaSpring

Liu, S., Guo, B., Ma, K., Yu, Z., & Du, J. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3448125 · Cited by 35

A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Gorospe, J., Mulero, R., Arbelaitz, O., Muguerza, J., & Antón, M. Á. (2021) · Sensors · doi:10.3390/s21041031 · Cited by 32

Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease

Zandehshahvar, M., van Assen, M., Maleki, H., Kiarashi, Y., Cecco, C. N. D., & Adibi, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-90411-3 · Cited by 30

Linearly Replaceable Filters for Deep Network Channel Pruning

Joo, D., Yi, E., Baek, S., & Kim, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i9.16978 · Cited by 32

Channel pruning guided by spatial and channel attention for DNNs in intelligent edge computing

Liu, M., Fang, W. W., Ma, X., Xu, W., Xiong, N. N., & Ding, Y. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107636 · Cited by 33

Development of a compressed FCN architecture for semantic segmentation using Particle Swarm Optimization

Agarwal, M., Gupta, S. K., & Biswas, K. K. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08324-3 · Cited by 32

Cardiovascular disease/stroke risk stratification in deep learning framework: a review

Bhagawati, M., Paul, S., Agarwal, S., Protogeron, A., Sfikakis, P. P., Kitas, G. D., Khanna, N. N., Ruzsa, Z., Sharma, A., Tomazu, O., et al. (2023) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt-22-438 · Cited by 35

Towards performance-maximizing neural network pruning via global channel attention

Wang, Y., Guo, S., Guo, J., Zhang, J., Zhang, W., Yan, C., & Zhang, Y. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.11.065 · Cited by 32

Pruning Very Deep Neural Network Channels for Efficient InferencePreprint

He, Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.08339 · Cited by 2

PruneNet: Channel Pruning via Global ImportancePreprint

Khetan, A., & Karnin, Z. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.11282 · Cited by 15

Feature Variance Ratio-Guided Channel Pruning for Deep Convolutional Network Acceleration

He, J., Chen, B., Ding, Y., & Li, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69538-5_11 · Cited by 2

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural NetworksPreprint

Zhang, Y., Zhao, C., Ni, B., Zhang, J., & Deng, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.02620 · Cited by 4

A Hybrid Statistics-based Channel Pruning Method for Deep CNNs

Zhou, Y., Liu, G., & Wang, D. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914512 · Cited by 2