Channel Pruning for Accelerating Very Deep Neural Networks

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Compressing Deep Convolutional Networks using Vector QuantizationPreprint

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Dynamic Network Surgery for Efficient DNNsPreprint

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Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep ArchitecturesPreprint

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Sparse Convolutional Neural Networks

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Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile ApplicationsPreprint

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Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning

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Structured Pruning of Deep Convolutional Neural Networks

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Speeding up Convolutional Neural Networks with Low Rank ExpansionsPreprint

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Data-free Parameter Pruning for Deep Neural Networks

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Speeding-up Convolutional Neural Networks Using Fine-tuned CP-DecompositionPreprint

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BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1Preprint

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Fast ConvNets Using Group-Wise Brain Damage

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Fast Training of Convolutional Networks through FFTs

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Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

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Learning the Number of Neurons in Deep NetworksPreprint

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Channel-level acceleration of deep face representations

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Structured Pruning of Deep Convolutional Neural NetworksPreprint

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Compact Deep Convolutional Neural Networks With Coarse PruningPreprint

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Diversity NetworksPreprint

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ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

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GhostNet: More Features From Cheap Operations

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RepVGG: Making VGG-style ConvNets Great Again

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The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

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FcaNet: Frequency Channel Attention Networks

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Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration

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Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

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Importance Estimation for Neural Network Pruning

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CondenseNet: An Efficient DenseNet Using Learned Group Convolutions

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A Survey of Model Compression and Acceleration for Deep Neural Networks

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HAQ: Hardware-Aware Automated Quantization With Mixed Precision

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Patient Knowledge Distillation for BERT Model Compression

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SkipNet: Learning Dynamic Routing in Convolutional Networks

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Bounding Box Regression With Uncertainty for Accurate Object Detection

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MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning

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Towards Optimal Structured CNN Pruning via Generative Adversarial Learning

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A systematic review on overfitting control in shallow and deep neural networks

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QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection

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Data-Driven Sparse Structure Selection for Deep Neural Networks

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Binary Neural Networks: A Survey

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DepGraph: Towards Any Structural Pruning

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A comprehensive survey on model compression and acceleration

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A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations

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Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments

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Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection

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Machine Learning at Facebook: Understanding Inference at the Edge

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Discrimination-aware channel pruning for deep neural networks

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SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning

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FFCA-YOLO for Small Object Detection in Remote Sensing Images

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A Survey of Recent Advances in Edge-Computing-Powered Artificial Intelligence of Things

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Data-Free Quantization Through Weight Equalization and Bias Correction

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Slimmable Neural NetworksPreprint

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ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions

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Reducing Transformer Depth on Demand with Structured Dropout

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MCUNet: Tiny Deep Learning on IoT DevicesPreprint

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Variational Convolutional Neural Network Pruning

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Structured Pruning for Deep Convolutional Neural Networks: A Survey

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Forward and Backward Information Retention for Accurate Binary Neural Networks

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EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction

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Knowledge Distillation via Instance Relationship Graph

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Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds

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DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

Rao, Y., Zhao, W., Liu, B., Lu, J., Zhou, J., & Hsieh, C.-J. (2021) · arXiv · doi:10.48550/arxiv.2106.02034 · Cited by 448 · see citing works

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

Matsubara, Y., Levorato, M., & Restuccia, F. (2022) · ACM Computing Surveys · doi:10.1145/3527155 · Cited by 327 · see citing works

An Efficient Specific Emitter Identification Method Based on Complex-Valued Neural Networks and Network Compression

Wang, Y., Gui, G., Gacanin, H., Ohtsuki, T. O., Dobre, O. A., & Poor, H. V. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3087243 · Cited by 315 · see citing works

Channel Pruning via Automatic Structure Search

Lin, M., Ji, R., Zhang, Y., Zhang, B., Wu, Y., & Tian, Y. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/94 · Cited by 277 · see citing works

Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—a Comparative Insight

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Lightweight Deep Learning for Resource-Constrained Environments: A Survey

Liu, H.-I., Galindo, M., Xie, H., Wong, L.-K., Shuai, H.-H., Li, Y., & Cheng, W.-H. (2024) · ACM Computing Surveys · doi:10.1145/3657282 · Cited by 296 · see citing works

Pruning by explaining: A novel criterion for deep neural network pruning

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ThiNet: Pruning CNN Filters for a Thinner Net

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Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration

He, Y., Ding, Y., Liu, P., Zhu, L., Zhang, H., & Yang, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00208 · Cited by 262 · see citing works

PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning

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Lightweight deep network for traffic sign classification

Zhang, J., Wang, W., Lu, C., Wang, J., & Sangaiah, A. K. (2019) · Annals of Telecommunications · doi:10.1007/s12243-019-00731-9 · Cited by 253 · see citing works

Deep Learning for SAR Ship Detection: Past, Present and Future

Li, J., Xu, C., Su, H., Gao, L., & Wang, T. (2022) · Remote Sensing · doi:10.3390/rs14112712 · Cited by 252 · see citing works

Dynamic Channel Pruning: Feature Boosting and SuppressionPreprint

Gao, X., Zhao, Y., Dudziak, Ł., Mullins, R., & Xu, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.05331 · Cited by 244 · see citing works

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

Ma, N., Zhang, X., Zheng, H.-T., & Sun, J. (2018) · arXiv · Cited by 7,344 · see citing works

A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification

Rokh, B., Azarpeyvand, A., & Khanteymoori, A. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3623402 · Cited by 254 · see citing works

Lightweight Underwater Object Detection Based on YOLO v4 and Multi-Scale Attentional Feature Fusion

Zhang, M., Xu, S., Song, W., He, Q., & Wei, Q. (2021) · Remote Sensing · doi:10.3390/rs13224706 · Cited by 237 · see citing works

Recent advances in efficient computation of deep convolutional neural networks

Cheng, J., Wang, P., Li, G., Hu, Q., & Lu, H. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700789 · Cited by 230 · see citing works

Methods for Pruning Deep Neural Networks

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Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

Coelho, C. N., Kuusela, A., Li, S., Zhuang, H., Ngadiuba, J., Aarrestad, T. K., Lončar, V., Maurizio Pierini, B., Pol, A. A., & Summers, S. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00356-5 · Cited by 225 · see citing works

Lightweight tomato real-time detection method based on improved YOLO and mobile deployment

Zeng, T., Li, S., Song, Q., Zhong, F., & Wei, X. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107625 · Cited by 225 · see citing works

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Capra, M., Bussolino, B., Marchisio, A., Masera, G., Martina, M., & Shafique, M. (2020) · IEEE Access · doi:10.1109/access.2020.3039858 · Cited by 228 · see citing works

DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks

Zhong, J., Chen, J., & Mian, A. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3151138 · Cited by 222 · see citing works

AutoPruner: An end-to-end trainable filter pruning method for efficient deep model inference

Luo, J.-H., & Wu, J. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107461 · Cited by 221 · see citing works

Highly Efficient Salient Object Detection with 100K Parameters

Gao, S., Tan, Y., Cheng, M., Lu, C.-Z., Chen, Y., & Yan, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58539-6_42 · Cited by 219 · see citing works

Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression

Li, Y., Gu, S., Mayer, C., Gool, L. V., & Timofte, R. (2020) · 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/cvpr42600.2020.00804 · Cited by 219 · see citing works

GAN Compression: Efficient Architectures for Interactive Conditional GANs

Li, M., Lin, J., Ding, Y., Liu, Z., Zhu, J.-Y., & Han, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00533 · Cited by 216 · see citing works

AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates

Liu, N., Ma, X., Xu, Z., Wang, Y., Tang, J., & Ye, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5924 · Cited by 206 · see citing works

Orthogonal Convolutional Neural Networks

Wang, J., Chen, Y., Chakraborty, R., & Yu, S. X. (2020) · 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/cvpr42600.2020.01152 · Cited by 202 · see citing works

Patch Slimming for Efficient Vision Transformers

Tang, Y., Han, K., Wang, Y., Xu, C., Guo, J., Xu, C., & Tao, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01185 · Cited by 201 · see citing works

SBNet: Sparse Blocks Network for Fast Inference

Ren, M., Pokrovsky, A., Yang, B., & Urtasun, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00908 · Cited by 198 · see citing works

Centripetal SGD for Pruning Very Deep Convolutional Networks With Complicated Structure

Ding, X., Ding, G., Guo, Y., & Han, J. (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.2019.00508 · Cited by 198 · see citing works

Adaptive Token Sampling for Efficient Vision Transformers

Fayyaz, M., Koohpayegani, S. A., Jafari, F., Sengupta, S., Joze, H. R. V., Sommerlade, E., Pirsiavash, H., & Gall, J. (2021) · European Conference on Computer Vision · doi:10.1007/978-3-031-20083-0_24 · Cited by 307 · see citing works

PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-Time Execution on Mobile Devices

Ma, X., Guo, F.-M., Niu, W., Lin, X., Tang, J., Ma, K., Ren, B., & Wang, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5954 · Cited by 196 · see citing works

ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting

Ding, X., Hao, T., Tan, J., Liu, J., Han, J., Guo, Y., & Ding, G. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00447 · Cited by 200 · see citing works

Tiny Machine Learning: Progress and Futures [Feature]

Lin, J., Zhu, L., Chen, W.-M., Wang, W.-C., & Han, S. (2023) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2023.3302182 · Cited by 216 · see citing works

GhostNets on Heterogeneous Devices via Cheap Operations

Han, K., Wang, Y., Xu, C., Guo, J., Xu, C., Wu, E., & Tian, Q. C. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01575-y · Cited by 198 · see citing works

Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks

He, Y., Dong, X., Kang, G., Fu, Y., Yan, C. C., & Yang, Y. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2933477 · Cited by 197 · see citing works

APQ: Joint Search for Network Architecture, Pruning and Quantization Policy

Wang, T., Wang, K., Cai, H., Lin, J., Liu, Z., Wang, H., Lin, Y., & Han, S. (2020) · 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/cvpr42600.2020.00215 · Cited by 190 · see citing works

Data-Distortion Guided Self-Distillation for Deep Neural Networks

Xu, T.-B., & Liu, C. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33015565 · Cited by 184 · see citing works

On the platform but will they buy? Predicting customers' purchase behavior using deep learning

Chaudhuri, N., Gupta, G., Vallurupalli, V., & Bose, I. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113622 · Cited by 184 · see citing works

EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning

Li, B., Wu, B., Su, J., & Wang, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58536-5_38 · Cited by 182 · see citing works

SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks

Akhlaghi, V., Yazdanbakhsh, A., Samadi, K., Gupta, R. K., & Esmaeilzadeh, H. (2018) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca.2018.00061 · Cited by 180 · see citing works

Towards Efficient Model Compression via Learned Global Ranking

Chin, T.-W., Ding, R., Zhang, C., & Marculescu, D. (2020) · 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/cvpr42600.2020.00159 · Cited by 180 · see citing works

Learning to Prune Filters in Convolutional Neural Networks

Huang, Q., Zhou, K., You, S. O., & Neumann, U. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00083 · Cited by 182 · see citing works

Network Pruning via Transformable Architecture SearchPreprint

Dong, X., & Yang, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.09717 · Cited by 177 · see citing works

BERT-of-Theseus: Compressing BERT by Progressive Module Replacing

Xu, C., Zhou, W., Ge, T., Wei, F., & Zhou, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.633 · Cited by 173 · see citing works

Elf

Zhang, W., He, Z., Liu, L., Jia, Z., Liu, Y., Gruteser, M., Raychaudhuri, D., & Zhang, Y. (2021) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3447993.3448628 · Cited by 173 · see citing works

Pruning filters with L1-norm and capped L1-norm for CNN compression

Kumar, A., Shaikh, A. M., Li, Y., Bilal, H., & Yin, B. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01894-y · Cited by 171 · see citing works

Pruning Very Deep Neural Network Channels for Efficient InferencePreprint

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

PruneNet: Channel Pruning via Global ImportancePreprint

Khetan, A., & Karnin, Z. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.11282 · Cited by 11 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works