14.5.10.7.8 Neural Net Pruning

Chen, S.[Shi], Zhao, Q.[Qi],
Shallowing Deep Networks: Layer-Wise Pruning Based on Feature Representations,
PAMI(41), No. 12, December 2019, pp. 3048-3056.
IEEE DOI 1911
Computational modeling, Computational efficiency, Feature extraction, Task analysis, Convolutional neural networks, convolutional neural networks BibRef

Singh, P.[Pravendra], Kadi, V.S.R.[Vinay Sameer Raja], Namboodiri, V.P.[Vinay P.],
FALF ConvNets: Fatuous auxiliary loss based filter-pruning for efficient deep CNNs,
IVC(93), 2020, pp. 103857.
Elsevier DOI 2001
Filter pruning, Model compression, Convolutional neural network, Image recognition, Deep learning BibRef

Singh, P.[Pravendra], Kadi, V.S.R.[Vinay Sameer Raja], Verma, N., Namboodiri, V.P.[Vinay P.],
Stability Based Filter Pruning for Accelerating Deep CNNs,
WACV19(1166-1174)
IEEE DOI 1904
computer networks, graphics processing units, learning (artificial intelligence), neural nets, Libraries BibRef

Mittal, D.[Deepak], Bhardwaj, S.[Shweta], Khapra, M.M.[Mitesh M.], Ravindran, B.[Balaraman],
Studying the plasticity in deep convolutional neural networks using random pruning,
MVA(30), No. 2, March 2019, pp. 203-216.
Springer DOI 1904
BibRef
Earlier:
Recovering from Random Pruning: On the Plasticity of Deep Convolutional Neural Networks,
WACV18(848-857)
IEEE DOI 1806
image classification, learning (artificial intelligence), neural nets, object detection, RCNN model, class specific pruning, Tuning BibRef

Bhardwaj, S.[Shweta], Srinivasan, M.[Mukundhan], Khapra, M.M.[Mitesh M.],
Efficient Video Classification Using Fewer Frames,
CVPR19(354-363).
IEEE DOI 2002
BibRef

Yang, W.Z.[Wen-Zhu], Jin, L.L.[Li-Lei], Wang, S.[Sile], Cu, Z.C.[Zhen-Chao], Chen, X.Y.[Xiang-Yang], Chen, L.P.[Li-Ping],
Thinning of convolutional neural network with mixed pruning,
IET-IPR(13), No. 5, 18 April 2019, pp. 779-784.
DOI Link 1904
BibRef

Luo, J.H.[Jian-Hao], Zhang, H.[Hao], Zhou, H.Y.[Hong-Yu], Xie, C.W.[Chen-Wei], Wu, J.X.[Jian-Xin], Lin, W.Y.[Wei-Yao],
ThiNet: Pruning CNN Filters for a Thinner Net,
PAMI(41), No. 10, October 2019, pp. 2525-2538.
IEEE DOI 1909
Convolution, Computational modeling, Task analysis, Acceleration, Training, Neural networks, Image coding, model compression BibRef

Ide, H.[Hidenori], Kobayashi, T.[Takumi], Watanabe, K.[Kenji], Kurita, T.[Takio],
Robust pruning for efficient CNNs,
PRL(135), 2020, pp. 90-98.
Elsevier DOI 2006
CNN, Pruning, Empirical classification loss, Taylor expansion BibRef

Kang, H.,
Accelerator-Aware Pruning for Convolutional Neural Networks,
CirSysVideo(30), No. 7, July 2020, pp. 2093-2103.
IEEE DOI 2007
Accelerator architectures, Field programmable gate arrays, Convolutional codes, Acceleration, Convolutional neural networks, neural network accelerator BibRef

Tsai, C.Y.[Chun-Ya], Gao, D.Q.[De-Qin], Ruan, S.J.[Shanq-Jang],
An effective hybrid pruning architecture of dynamic convolution for surveillance videos,
JVCIR(70), 2020, pp. 102798.
Elsevier DOI 2007
Optimize CNN, Dynamic convolution, Pruning, Smart surveillance application BibRef

Wang, Z., Hong, W., Tan, Y., Yuan, J.,
Pruning 3D Filters For Accelerating 3D ConvNets,
MultMed(22), No. 8, August 2020, pp. 2126-2137.
IEEE DOI 2007
Acceleration, Feature extraction, Task analysis, Maximum Abs. of Filters (MAF) BibRef

Luo, J.H.[Jian-Hao], Wu, J.X.[Jian-Xin],
AutoPruner: An end-to-end trainable filter pruning method for efficient deep model inference,
PR(107), 2020, pp. 107461.
Elsevier DOI 2008
Neural network pruning, Model compression, CNN acceleration BibRef

Ding, G., Zhang, S., Jia, Z., Zhong, J., Han, J.,
Where to Prune: Using LSTM to Guide Data-Dependent Soft Pruning,
IP(30), 2021, pp. 293-304.
IEEE DOI 2012
Computational modeling, Reinforcement learning, Image coding, Training, Convolution, Tensors, image classification BibRef

Tian, Q.[Qing], Arbel, T.[Tal], Clark, J.J.[James J.],
Task dependent deep LDA pruning of neural networks,
CVIU(203), 2021, pp. 103154.
Elsevier DOI 2101
Deep neural networks pruning, Deep linear discriminant analysis, Deep feature learning BibRef

Tian, G., Chen, J., Zeng, X., Liu, Y.,
Pruning by Training: A Novel Deep Neural Network Compression Framework for Image Processing,
SPLetters(28), 2021, pp. 344-348.
IEEE DOI 2102
Collaboration, Training, Computational modeling, Neural networks, Convolution, Optimization, Size measurement, model compression BibRef

Guo, J.Y.[Jin-Yang], Zhang, W.C.[Wei-Chen], Ouyang, W.L.[Wan-Li], Xu, D.[Dong],
Model Compression Using Progressive Channel Pruning,
CirSysVideo(31), No. 3, March 2021, pp. 1114-1124.
IEEE DOI 2103
Acceleration, Adaptation models, Convolution, Supervised learning, Neural networks, Computational modeling, Model compression, transfer learning BibRef

Alqahtani, A.[Ali], Xie, X.H.[Xiang-Hua], Jones, M.W.[Mark W.], Essa, E.[Ehab],
Pruning CNN filters via quantifying the importance of deep visual representations,
CVIU(208-209), 2021, pp. 103220.
Elsevier DOI 2106
BibRef
Earlier: A1, A2, A4, A3:
Neuron-based Network Pruning Based on Majority Voting,
ICPR21(3090-3097)
IEEE DOI 2105
Deep learning, Convolutional neural networks, Filter pruning, Model compression. Training, Neurons, Memory management, Pattern recognition, Computational efficiency, Complexity theory BibRef

Wang, Y.[Yooseung], Park, H.[Hyunseong], Lee, J.[Jwajin],
Memory-Free Stochastic Weight Averaging by One-Way Variational Pruning,
SPLetters(28), 2021, pp. 1021-1025.
IEEE DOI 2106
Training, Computational modeling, Stochastic processes, Brain modeling, Trajectory, Mathematical model, neural network pruning BibRef

Li, G.[Guo], Xu, G.[Gang],
Providing clear pruning threshold: A novel CNN pruning method via L0 regularisation,
IET-IPR(15), No. 2, 2021, pp. 405-418.
DOI Link 2106
BibRef

Liu, Y.X.[Yi-Xin], Guo, Y.[Yong], Guo, J.X.[Jia-Xin], Jiang, L.Q.[Luo-Qian], Chen, J.[Jian],
Conditional Automated Channel Pruning for Deep Neural Networks,
SPLetters(28), 2021, pp. 1275-1279.
IEEE DOI 2107
Computational modeling, Image coding, Optimization, Markov processes, Signal processing algorithms, Search problems, model compression BibRef

Osaku, D., Gomes, J.F., Falcão, A.X.,
Convolutional neural network simplification with progressive retraining,
PRL(150), 2021, pp. 235-241.
Elsevier DOI 2109
Kernel pruning, Deep learning, Image classification BibRef

Fan, F.G.[Fu-Gui], Su, Y.T.[Yu-Ting], Jing, P.G.[Pei-Guang], Lu, W.[Wei],
A Dual Rank-Constrained Filter Pruning Approach for Convolutional Neural Networks,
SPLetters(28), 2021, pp. 1734-1738.
IEEE DOI 2109
Manifolds, Adaptation models, Correlation, Adaptive systems, Adaptive filters, Information filters, high-rank BibRef

Liu, Z.C.[Ze-Chun], Zhang, X.Y.[Xiang-Yu], Shen, Z.Q.[Zhi-Qiang], Wei, Y.C.[Yi-Chen], Cheng, K.T.[Kwang-Ting], Sun, J.[Jian],
Joint Multi-Dimension Pruning via Numerical Gradient Update,
IP(30), 2021, pp. 8034-8045.
IEEE DOI 2109
Estimation, Numerical models, Optimization, Training, Task analysis, Spatial resolution, Adaptation models, multi-dimension BibRef

Tan, J.H.[Jia Huei], Chan, C.S.[Chee Seng], Chuah, J.H.[Joon Huang],
End-to-End Supermask Pruning: Learning to Prune Image Captioning Models,
PR(122), 2022, pp. 108366.
Elsevier DOI 2112
Image captioning, Deep network compression, Deep learning BibRef

Liu, Z.F.[Zhou-Feng], Liu, X.H.[Xiao-Hui], Li, C.L.[Chun-Lei], Ding, S.M.[Shu-Min], Liao, L.[Liang],
Learning compact ConvNets through filter pruning based on the saliency of a feature map,
IET-IPR(16), No. 1, 2022, pp. 123-133.
DOI Link 2112
BibRef

Ioannidis, V.N.[Vassilis N.], Chen, S.[Siheng], Giannakis, G.B.[Georgios B.],
Efficient and Stable Graph Scattering Transforms via Pruning,
PAMI(44), No. 3, March 2022, pp. 1232-1246.
IEEE DOI 2202
Scattering, Transforms, Feature extraction, Stability analysis, Perturbation methods, Convolution BibRef

Tofigh, S.[Sadegh], Ahmad, M.O.[M. Omair], Swamy, M.N.S.,
A Low-Complexity Modified ThiNet Algorithm for Pruning Convolutional Neural Networks,
SPLetters(29), 2022, pp. 1012-1016.
IEEE DOI 2205
Signal processing algorithms, Convolution, Convolutional neural networks, Training, Testing, Tensors, ThiNet algorithm BibRef

Zhang, K.[Ke], Liu, G.Z.[Guang-Zhe],
Layer Pruning for Obtaining Shallower ResNets,
SPLetters(29), 2022, pp. 1172-1176.
IEEE DOI 2205
Training, Taylor series, Convolution, Cost function, Sorting, Signal processing algorithms, Kernel, Layer pruning, network pruning BibRef

Wang, H.Y.[Huan-Yu], Zhang, Y.S.[Yong-Shun], Wu, J.X.[Jian-Xin],
Versatile, full-spectrum, and swift network sampling for model generation,
PR(129), 2022, pp. 108729.
Elsevier DOI 2206
Model generation, Convolutional neural networks, Structured pruning, Model compression BibRef

Liu, J.[Jing], Zhuang, B.[Bohan], Zhuang, Z.W.[Zhuang-Wei], Guo, Y.[Yong], Huang, J.Z.[Jun-Zhou], Zhu, J.H.[Jin-Hui], Tan, M.K.[Ming-Kui],
Discrimination-Aware Network Pruning for Deep Model Compression,
PAMI(44), No. 8, August 2022, pp. 4035-4051.
IEEE DOI 2207
Kernel, Computational modeling, Quantization (signal), Training, Adaptation models, Acceleration, Redundancy, Channel pruning, deep neural networks BibRef

Mondal, M.[Milton], Das, B.[Bishshoy], Roy, S.D.[Sumantra Dutta], Singh, P.[Pushpendra], Lall, B.[Brejesh], Joshi, S.D.[Shiv Dutt],
Adaptive CNN filter pruning using global importance metric,
CVIU(222), 2022, pp. 103511.
Elsevier DOI 2209
Adaptive pruning, Convolutional neural networks, Filter pruning, Model compression BibRef

Camci, E.[Efe], Gupta, M.[Manas], Wu, M.[Min], Lin, J.[Jie],
QLP: Deep Q-Learning for Pruning Deep Neural Networks,
CirSysVideo(32), No. 10, October 2022, pp. 6488-6501.
IEEE DOI 2210
Training, Neural networks, Indexes, Deep learning, Biological neural networks, Task analysis, deep reinforcement learning BibRef

Zhang, H.[Haonan], Liu, L.[Longjun], Zhou, H.[Hengyi], Si, L.[Liang], Sun, H.B.[Hong-Bin], Zheng, N.N.[Nan-Ning],
FCHP: Exploring the Discriminative Feature and Feature Correlation of Feature Maps for Hierarchical DNN Pruning and Compression,
CirSysVideo(32), No. 10, October 2022, pp. 6807-6820.
IEEE DOI 2210
Frequency modulation, Correlation, Feature extraction, Matrix decomposition, Indexes, Neural networks, Sparse matrices, feature correlation BibRef

Li, Y.[Yun], Liu, Z.[Zechun], Wu, W.[Weiqun], Yao, H.[Haotian], Zhang, X.Y.[Xiang-Yu], Zhang, C.[Chi], Yin, B.[Baoqun],
Weight-Dependent Gates for Network Pruning,
CirSysVideo(32), No. 10, October 2022, pp. 6941-6954.
IEEE DOI 2210
Logic gates, Information filters, Encoding, Hardware, Switches, Training, Manuals, Weight-dependent gates, network pruning BibRef

Wang, J.L.[Jie-Lei], Cui, Z.Y.[Zong-Yong], Zang, Z.P.[Zhi-Peng], Meng, X.J.[Xiang-Jie], Cao, Z.[Zongjie],
Absorption Pruning of Deep Neural Network for Object Detection in Remote Sensing Imagery,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Yvinec, E.[Edouard], Dapogny, A.[Arnaud], Cord, M.[Matthieu], Bailly, K.[Kevin],
RED++: Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging,
PAMI(45), No. 3, March 2023, pp. 3664-3676.
IEEE DOI 2302
Neurons, Merging, Redundancy, Training, Quantization (signal), Kernel, Tensors, Deep learning, pruning, data-free, machine learning, neural networks BibRef

Lin, M.B.[Ming-Bao], Zhang, Y.X.[Yu-Xin], Li, Y.C.[Yu-Chao], Chen, B.[Bohong], Chao, F.[Fei], Wang, M.D.[Meng-Di], Li, S.[Shen], Tian, Y.H.[Yong-Hong], Ji, R.R.[Rong-Rong],
1xN Pattern for Pruning Convolutional Neural Networks,
PAMI(45), No. 4, April 2023, pp. 3999-4008.
IEEE DOI 2303
Kernel, Indexes, Convolutional neural networks, Training, Shape, Filtering algorithms, Convolution, Network pruning, CNNs BibRef

Zhong, Y.S.[Yun-Shan], Lin, M.B.[Ming-Bao], Chen, M.Z.[Meng-Zhao], Li, K.[Ke], Shen, Y.H.[Yun-Hang], Chao, F.[Fei], Wu, Y.J.[Yong-Jian], Ji, R.R.[Rong-Rong],
Fine-grained Data Distribution Alignment for Post-Training Quantization,
ECCV22(XI:70-86).
Springer DOI 2211
BibRef

Long, X.[Xin], Zeng, X.R.[Xiang-Rong], Liu, Y.[Yu], Qiao, M.[Mu],
Low Bit Neural Networks with Channel Sparsity and Sharing,
ICIVC22(889-894)
IEEE DOI 2301
Training, Visualization, Tensors, Quantization (signal), Computational modeling, OWL, Redundancy, Ordered weighted l1, Sharing BibRef

Massart, E.[Estelle],
Improving weight clipping in Wasserstein GANs,
ICPR22(2286-2292)
IEEE DOI 2212
Training, Convolution, Generators, Numerical models, Standards BibRef

Zhao, Y.[Yu], Lee, C.K.[Chung-Kuei],
Differentiable Channel Sparsity Search via Weight Sharing within Filters,
ICPR22(2012-2018)
IEEE DOI 2212

WWW Link. Image resolution, Shape, Semantic segmentation, Memory management, Filtering algorithms, Information filters, Stability analysis BibRef

Xie, X.[Xiang], Chen, T.T.[Tian-Tian], Chu, A.[Anqi], Stork, W.[Wilhelm],
Efficient Network Pruning via Feature Selection,
ICPR22(1843-1850)
IEEE DOI 2212
Training, Redundancy, Neurons, Network architecture, Feature extraction, Convolutional neural networks, Time factors BibRef

Malik, S.[Shehryar], Haider, M.U.[Muhammad Umair], Iqbal, O.[Omer], Taj, M.[Murtaza],
Neural Network Pruning Through Constrained Reinforcement Learning,
ICPR22(3027-3033)
IEEE DOI 2212
Training, Measurement, Neurons, Memory management, Reinforcement learning, Biological neural networks BibRef

Sakai, Y.[Yasufumi], Iwakawa, A.[Akinori], Tabaru, T.[Tsuguchika], Inoue, A.[Atsuki], Kawaguchi, H.[Hiroshi],
Automatic Pruning Rate Derivation for Structured Pruning of Deep Neural Networks,
ICPR22(2561-2567)
IEEE DOI 2212
Degradation, Deep learning, Image coding, Neural networks, Bit error rate, Manuals, Transformers, Neural networks, Automatic pruning rate search BibRef

McDanel, B.[Bradley], Dinh, H.[Helia], Magallanes, J.[John],
Accelerating DNN Training with Structured Data Gradient Pruning,
ICPR22(2293-2299)
IEEE DOI 2212
Training, Deep learning, Computational modeling, Source coding, Neural networks, Graphics processing units, Data models BibRef

Famili, A.[Azadeh], Lao, Y.J.[Ying-Jie],
Genetic-based Joint Dynamic Pruning and Learning Algorithm to Boost DNN Performance,
ICPR22(2100-2106)
IEEE DOI 2212
Training, Heuristic algorithms, Neurons, Biological systems, Inference algorithms, Stability analysis BibRef

Dupont, R.[Robin], Alaoui, M.A.[Mohammed Amine], Sahbi, H.[Hichem], Lebois, A.[Alice],
Extracting Effective Subnetworks with Gumbel-Softmax,
ICIP22(931-935)
IEEE DOI 2211
Weight measurement, Training, Network topology, Scalability, Neural networks, Probability distribution, Lightweight networks, topology selection BibRef

Humble, R.[Ryan], Shen, M.[Maying], Latorre, J.A.[Jorge Albericio], Darve, E.[Eric], Alvarez, J.[Jose],
Soft Masking for Cost-Constrained Channel Pruning,
ECCV22(XI:641-657).
Springer DOI 2211
BibRef

Gao, S.Q.[Shang-Qian], Huang, F.H.[Fei-Hu], Zhang, Y.F.[Yan-Fu], Huang, H.[Heng],
Disentangled Differentiable Network Pruning,
ECCV22(XI:328-345).
Springer DOI 2211
BibRef

Lee, S.H.[Seung-Hyun], Song, B.C.[Byung Cheol],
Ensemble Knowledge Guided Sub-network Search and Fine-Tuning for Filter Pruning,
ECCV22(XI:569-585).
Springer DOI 2211
BibRef

Kim, T.[Taeho], Kwon, Y.[Yongin], Lee, J.[Jemin], Kim, T.[Taeho], Ha, S.[Sangtae],
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution,
ECCV22(XX:651-667).
Springer DOI 2211
BibRef

He, Z.Q.[Zhi-Qiang], Qian, Y.G.[Ya-Guan], Wang, Y.[Yuqi], Wang, B.[Bin], Guan, X.H.[Xiao-Hui], Gu, Z.Q.[Zhao-Quan], Ling, X.[Xiang], Zeng, S.N.[Shao-Ning], Wang, H.J.[Hai-Jiang], Zhou, W.[Wujie],
Filter Pruning via Feature Discrimination in Deep Neural Networks,
ECCV22(XXI:245-261).
Springer DOI 2211
BibRef

Ganjdanesh, A.[Alireza], Gao, S.Q.[Shang-Qian], Huang, H.[Heng],
Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps,
ECCV22(XXI:278-296).
Springer DOI 2211
BibRef

Fan, H.[Hanwei], Mu, J.[Jiandong], Zhang, W.[Wei],
Bayesian Optimization with Clustering and Rollback for CNN Auto Pruning,
ECCV22(XXIII:494-511).
Springer DOI 2211
BibRef

Saniee, I.[Iraj], Zhang, L.[Lisa], Magnetta, B.[Bradley],
Truncated Lottery Ticket for Deep Pruning,
ICIP22(606-610)
IEEE DOI 2211
Deep learning, Quantization (signal), Image coding, Image edge detection, Neural networks, Reduced order systems, lottery ticket hypothesis BibRef

Joo, D.G.[Dong-Gyu], Baek, S.[Sunghyun], Kim, J.[Junmo],
Which Metrics for Network Pruning: Final Accuracy? Or Accuracy Drop?,
ICIP22(1071-1075)
IEEE DOI 2211
Measurement, Deep learning, Image coding, Correlation, Computer network reliability, Neural networks, Reliability, Evaluation Metric BibRef

Mille, J.[Julien],
Convex Quadratic Programming for Slimming Convolutional Networks,
ICIP22(1121-1125)
IEEE DOI 2211
Costs, Linear programming, Quadratic programming, Image reconstruction, Optimization, Pruning, ConvNet, convex optimization BibRef

Tartaglione, E.[Enzo],
The Rise of the Lottery Heroes: Why Zero-Shot Pruning is Hard,
ICIP22(2361-2365)
IEEE DOI 2211
Training, Deep learning, Computational modeling, Complexity theory, Optimization, The lottery ticket hypothesis, pruning, deep learning BibRef

Flich, J.[José], Medina, L.[Laura], Catalán, I.[Izan], Hernández, C.[Carles], Bragagnolo, A.[Andrea], Auzanneau, F.[Fabrice], Briand, D.[David],
Efficient Inference Of Image-Based Neural Network Models In Reconfigurable Systems With Pruning And Quantization,
ICIP22(2491-2495)
IEEE DOI 2211
Quantization (signal), Embedded systems, Image coding, Computational modeling, Artificial neural networks, Libraries, inference BibRef

Sahbi, H.[Hichem],
Topologically-Consistent Magnitude Pruning for Very Lightweight Graph Convolutional Networks,
ICIP22(3495-3499)
IEEE DOI 2211
Image recognition, Convolution, Message passing, Network architecture, Task analysis, Context modeling, skeleton-based recognition BibRef

Sharma, A.[Ankit], Foroosh, H.[Hassan],
RAPID: A Single Stage Pruning Framework,
ICIP22(3611-3615)
IEEE DOI 2211
Training, Knowledge engineering, Deep learning, Quantization (signal), Computational modeling, Pipelines, Distillation BibRef

Tayyab, M.[Muhammad], Mahalanobis, A.[Abhijit],
Simultaneous Learning and Compression for Convolution Neural Networks,
ICIP22(3636-3640)
IEEE DOI 2211
Training, Maximum likelihood detection, Image coding, Convolution, Nonlinear filters, Neural network compression, Pruning BibRef

Hubens, N.[Nathan], Mancas, M.[Matei], Gosselin, B.[Bernard], Preda, M.[Marius], Zaharia, T.[Titus],
One-Cycle Pruning: Pruning Convnets With Tight Training Budget,
ICIP22(4128-4132)
IEEE DOI 2211
Training, Schedules, Image coding, Pipelines, Neural networks, Complexity theory, Neural Network Pruning, Pruning Schedule BibRef

Zhao, T.L.[Tian-Li], Zhang, X.S.[Xi Sheryl], Zhu, W.T.[Wen-Tao], Wang, J.X.[Jia-Xing], Yang, S.[Sen], Liu, J.[Ji], Cheng, J.[Jian],
Multi-granularity Pruning for Model Acceleration on Mobile Devices,
ECCV22(XI:484-501).
Springer DOI 2211
BibRef

Li, Y.[Yawei], Adamczewski, K.[Kamil], Li, W.[Wen], Gu, S.H.[Shu-Hang], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
Revisiting Random Channel Pruning for Neural Network Compression,
CVPR22(191-201)
IEEE DOI 2210
Training, Neural network compression, Benchmark testing, Network architecture, Filtering algorithms, Machine learning, retrieval BibRef

Shen, M.[Maying], Molchanov, P.[Pavlo], Yin, H.X.[Hong-Xu], Alvarez, J.M.[Jose M.],
When to Prune? A Policy towards Early Structural Pruning,
CVPR22(12237-12246)
IEEE DOI 2210
Training, Measurement, Costs, Neurons, Object detection, Power system stability, Efficient learning and inferences BibRef

Elkerdawy, S.[Sara], Elhoushi, M.[Mostafa], Zhang, H.[Hong], Ray, N.[Nilanjan],
Fire Together Wire Together: A Dynamic Pruning Approach with Self-Supervised Mask Prediction,
CVPR22(12444-12453)
IEEE DOI 2210
Heating systems, Training, Head, Neuroscience, Wires, Predictive models, Deep learning architectures and techniques BibRef

Wimmer, P.[Paul], Mehnert, J.[Jens], Condurache, A.[Alexandru],
Interspace Pruning: Using Adaptive Filter Representations to Improve Training of Sparse CNNs,
CVPR22(12517-12527)
IEEE DOI 2210
Training, Deep learning, Maximum likelihood detection, Runtime, Adaptive filters, Optimization methods, Nonlinear filters, Optimization methods BibRef

Sun, W.Y.[Wen-Yu], Cao, J.[Jian], Xu, P.[Pengtao], Liu, X.[Xiangcheng], Zhang, Y.[Yuan], Wang, Y.[Yuan],
An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution,
ECV22(2608-2617)
IEEE DOI 2210
Training, Image resolution, Adaptive systems, Image edge detection, Energy resolution, Object detection, Computer architecture BibRef

Pan, S.Y.[Si-Yuan], Qin, Y.M.[Yi-Ming], Li, T.Y.[Ting-Yao], Li, X.S.[Xiao-Shuang], Hou, L.[Liang],
Momentum Contrastive Pruning,
ECV22(2646-2655)
IEEE DOI 2210
Representation learning, Visualization, Computational modeling, Supervised learning, Self-supervised learning BibRef

Srinivas, S.[Suraj], Kuzmin, A.[Andrey], Nagel, M.[Markus], van Baalen, M.[Mart], Skliar, A.[Andrii], Blankevoort, T.[Tijmen],
Cyclical Pruning for Sparse Neural Networks,
ECV22(2761-2770)
IEEE DOI 2210
Deep learning, Schedules, Computational modeling, Neural networks, Pipelines BibRef

Joo, D.G.[Dong-Gyu], Kim, D.[Doyeon], Yi, E.[Eojindl], Kim, J.[Junmo],
Linear Combination Approximation of Feature for Channel Pruning,
ECV22(2771-2780)
IEEE DOI 2210
Deep learning, Correlation, Convolution, Neural networks, Linearity BibRef

dos Santos, C.F.G.[Claudio Filipi Goncalves], Roder, M.[Mateus], Passos, L.A.[Leandro Aparecido], Papa, J.P.[João Paulo],
MaxDropoutV2: An Improved Method to Drop Out Neurons in Convolutional Neural Networks,
IbPRIA22(271-282).
Springer DOI 2205
BibRef

Hubens, N.[Nathan], Mancas, M.[Matei], Gosselin, B.[Bernard], Preda, M.[Marius], Zaharia, T.[Titus],
Improve Convolutional Neural Network Pruning by Maximizing Filter Variety,
CIAP22(I:379-390).
Springer DOI 2205
BibRef

Merkle, F.[Florian], Samsinger, M.[Maximilian], Schöttle, P.[Pascal],
Pruning in the Face of Adversaries,
CIAP22(I:658-669).
Springer DOI 2205
BibRef

Liu, F.X.[Fang-Xin], Zhao, W.[Wenbo], He, Z.[Zhezhi], Wang, Y.Z.[Yan-Zhi], Wang, Z.[Zongwu], Dai, C.Z.[Chang-Zhi], Liang, X.Y.[Xiao-Yao], Jiang, L.[Li],
Improving Neural Network Efficiency via Post-training Quantization with Adaptive Floating-Point,
ICCV21(5261-5270)
IEEE DOI 2203
Degradation, Training, Adaptation models, Energy consumption, Quantization (signal), Computational modeling, Encoding, Recognition and classification BibRef

Kim, D.[Dohyung], Lee, J.[Junghyup], Ham, B.[Bumsub],
Distance-aware Quantization,
ICCV21(5251-5260)
IEEE DOI 2203
Training, Quantization (signal), Data acquisition, Network architecture, Benchmark testing, Temperature control, Representation learning BibRef

Han, T.T.[Tian-Tian], Li, D.[Dong], Liu, J.[Ji], Tian, L.[Lu], Shan, Y.[Yi],
Improving Low-Precision Network Quantization via Bin Regularization,
ICCV21(5241-5250)
IEEE DOI 2203
Training, Deep learning, Quantization (signal), Computational modeling, Neural networks, Network architecture, Recognition and classification BibRef

Bulat, A.[Adrian], Tzimiropoulos, G.[Georgios],
Bit-Mixer: Mixed-precision networks with runtime bit-width selection,
ICCV21(5168-5177)
IEEE DOI 2203
Training, Knowledge engineering, Runtime, Quantization (signal), Codes, Pipelines, Efficient training and inference methods, BibRef

Xu, Z.[Zihan], Lin, M.[Mingbao], Liu, J.Z.[Jian-Zhuang], Chen, J.[Jie], Shao, L.[Ling], Gao, Y.[Yue], Tian, Y.H.[Yong-Hong], Ji, R.R.[Rong-Rong],
ReCU: Reviving the Dead Weights in Binary Neural Networks,
ICCV21(5178-5188)
IEEE DOI 2203
Training, Quantization (signal), Codes, Neural networks, Standardization, Clamps, BibRef

Chen, P.[Peng], Zhuang, B.[Bohan], Shen, C.H.[Chun-Hua],
FATNN: Fast and Accurate Ternary Neural Networks*,
ICCV21(5199-5208)
IEEE DOI 2203
Quantization (signal), Neural networks, Object detection, Benchmark testing, Network architecture, Transformers, Representation learning BibRef

Gu, J.Q.[Jia-Qi], Zhu, H.Q.[Han-Qing], Feng, C.H.[Cheng-Hao], Liu, M.J.[Ming-Jie], Jiang, Z.X.[Zi-Xuan], Chen, R.T.[Ray T.], Pan, D.Z.[David Z.],
Towards Memory-Efficient Neural Networks via Multi-Level in situ Generation,
ICCV21(5209-5218)
IEEE DOI 2203
Correlation, Quantization (signal), Computational modeling, Memory management, Redundancy, Neural networks, System-on-chip, BibRef

Chang, S.E.[Sung-En], Li, Y.[Yanyu], Sun, M.[Mengshu], Jiang, W.[Weiwen], Liu, S.[Sijia], Wang, Y.Z.[Yan-Zhi], Lin, X.[Xue],
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions,
ICCV21(5231-5240)
IEEE DOI 2203
Performance evaluation, Deep learning, Quantization (signal), Neural networks, Search problems, Hardware, Recognition and classification BibRef

Chen, W.H.[Wei-Han], Wang, P.S.[Pei-Song], Cheng, J.[Jian],
Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimization,
ICCV21(5330-5339)
IEEE DOI 2203
Degradation, Deep learning, Quantization (signal), Neural networks, Network architecture, Search problems, Taylor series, BibRef

Sun, X.[Ximeng], Panda, R.[Rameswar], Chen, C.F.R.[Chun-Fu Richard], Oliva, A.[Aude], Feris, R.[Rogerio], Saenko, K.[Kate],
Dynamic Network Quantization for Efficient Video Inference,
ICCV21(7355-7365)
IEEE DOI 2203
Backpropagation, Quantization (signal), Benchmark testing, Boosting, Standards, Video analysis and understanding, Efficient training and inference methods BibRef

Lin, H.[Haowen], Lou, J.[Jian], Xiong, L.[Li], Shahabi, C.[Cyrus],
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks,
ICCV21(7808-7817)
IEEE DOI 2203
Smoothing methods, Program processors, Tensors, Quantization (signal), Computational modeling, Neural networks, Efficient training and inference methods BibRef

Wang, Y.K.[Yi-Kai], Yang, Y.[Yi], Sun, F.C.[Fu-Chun], Yao, A.[Anbang],
Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks,
ICCV21(5340-5349)
IEEE DOI 2203
Convolutional codes, Training, Visualization, Quantization (signal), Image coding, Runtime, Image recognition, Representation learning BibRef

Lee, J.H.[Jung Hyun], Yun, J.[Jihun], Hwang, S.J.[Sung Ju], Yang, E.[Eunho],
Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss,
ICCV21(5350-5359)
IEEE DOI 2203
Training, Performance evaluation, Quantization (signal), Neurons, Neural networks, Network architecture, BibRef

Shen, M.Z.[Ming-Zhu], Liang, F.[Feng], Gong, R.[Ruihao], Li, Y.H.[Yu-Hang], Li, C.M.[Chu-Ming], Lin, C.[Chen], Yu, F.W.[Feng-Wei], Yan, J.J.[Jun-Jie], Ouyang, W.L.[Wan-Li],
Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search,
ICCV21(5320-5329)
IEEE DOI 2203
Training, Degradation, Quantization (signal), Costs, Computational modeling, Neural networks, BibRef

Guo, Y.[Yi], Yuan, H.[Huan], Tan, J.C.[Jian-Chao], Wang, Z.Y.[Zhang-Yang], Yang, S.[Sen], Liu, J.[Ji],
GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization,
ICCV21(5219-5230)
IEEE DOI 2203
Training, Image segmentation, Economic indicators, Neural networks, Logic gates, Benchmark testing, Real-time systems, Optimization and learning methods BibRef

Ding, X.H.[Xiao-Han], Hao, T.X.[Tian-Xiang], Tan, J.C.[Jian-Chao], Liu, J.[Ji], Han, J.G.[Jun-Gong], Guo, Y.C.[Yu-Chen], Ding, G.[Guiguang],
ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting,
ICCV21(4490-4500)
IEEE DOI 2203
Training, Convolutional codes, Image coding, Standards, Efficient training and inference methods, BibRef

Wu, Y.S.[Yu-Shu], Gong, Y.F.[Yi-Fan], Zhao, P.[Pu], Li, Y.[Yanyu], Zhan, Z.[Zheng], Niu, W.[Wei], Tang, H.[Hao], Qin, M.[Minghai], Ren, B.[Bin], Wang, Y.Z.[Yan-Zhi],
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution,
ECCV22(XIX:92-111).
Springer DOI 2211
BibRef

Zhan, Z.[Zheng], Gong, Y.F.[Yi-Fan], Zhao, P.[Pu], Yuan, G.[Geng], Niu, W.[Wei], Wu, Y.S.[Yu-Shu], Zhang, T.[Tianyun], Jayaweera, M.[Malith], Kaeli, D.[David], Ren, B.[Bin], Lin, X.[Xue], Wang, Y.Z.[Yan-Zhi],
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search,
ICCV21(4801-4811)
IEEE DOI 2203
Image quality, Deep learning, Computational modeling, Superresolution, Neural networks, Memory management, Vision applications and systems BibRef

Yu, S.X.[Si-Xing], Mazaheri, A.[Arya], Jannesari, A.[Ali],
Auto Graph Encoder-Decoder for Neural Network Pruning,
ICCV21(6342-6352)
IEEE DOI 2203
Learning systems, Deep learning, Computational modeling, Reinforcement learning, Mobile handsets, Graph neural networks, BibRef

Wang, Z.[Zi], Li, C.C.[Cheng-Cheng],
Channel Pruning via Lookahead Search Guided Reinforcement Learning,
WACV22(3513-3524)
IEEE DOI 2202
Training, Degradation, Monte Carlo methods, Neural networks, Reinforcement learning, Benchmark testing, Deep Learning Deep Learning -> Efficient Training and Inference Methods for Networks BibRef

Lin, R.[Rui], Ran, J.[Jie], Wang, D.P.[Dong-Peng], Chiu, K.H.[King Hung], Wong, N.[Ngai],
EZCrop: Energy-Zoned Channels for Robust Output Pruning,
WACV22(3595-3604)
IEEE DOI 2202
Runtime, Codes, Fast Fourier transforms, Frequency-domain analysis, Robustness, Computational efficiency, Analysis and Understanding BibRef

Hickson, S.[Steven], Raveendran, K.[Karthik], Essa, I.[Irfan],
Sharing Decoders: Network Fission for Multi-task Pixel Prediction,
WACV22(3655-3664)
IEEE DOI 2202
Semantics, Memory management, Prediction methods, Multitasking, Real-time systems, Mobile handsets, Decoding, Vision Systems and Applications BibRef

Yu, S.X.[Shi-Xing], Yao, Z.[Zhewei], Gholami, A.[Amir], Dong, Z.[Zhen], Kim, S.H.[Se-Hoon], Mahoney, M.W.[Michael W.], Keutzer, K.[Kurt],
Hessian-Aware Pruning and Optimal Neural Implant,
WACV22(3665-3676)
IEEE DOI 2202
Degradation, Sensitivity, Head, Natural languages, Neural implants, Transformers, Deep Learning -> Efficient Training and Inference Methods for Networks BibRef

Bragagnolo, A.[Andrea], Tartaglione, E.[Enzo], Fiandrotti, A.[Attilio], Grangetto, M.[Marco],
On the Role of Structured Pruning for Neural Network Compression,
ICIP21(3527-3531)
IEEE DOI 2201
Image coding, Tensors, Neural networks, Pipelines, Transform coding, Standardization, Pruning, Deep learning, Compression, MPEG-7 BibRef

Tavakoli, H.R.[Hamed R.], Wabnig, J.[Joachim], Cricri, F.[Francesco], Zhang, H.L.[Hong-Lei], Aksu, E.[Emre], Saniee, I.[Iraj],
Hybrid Pruning and Sparsification,
ICIP21(3542-3546)
IEEE DOI 2201
Deep learning, Image coding, Convolution, Neurons, Diffusion processes, Network architecture, graph diffusion BibRef

Haider, M.U.[Muhammad Umair], Taj, M.[Murtaza],
Comprehensive Online Network Pruning Via Learnable Scaling Factors,
ICIP21(3557-3561)
IEEE DOI 2201
Deep learning, Image coding, Neurons, Memory management, Logic gates, Benchmark testing, Neural Networks, synaptic pruning, recognition BibRef

Retsinas, G.[George], Elafrou, A.[Athena], Goumas, G.[Georgios], Maragos, P.[Petros],
Online Weight Pruning Via Adaptive Sparsity Loss,
ICIP21(3517-3521)
IEEE DOI 2201
Training, Deep learning, Adaptive systems, Image coding, Neural networks, Network architecture, Weight Pruning, Budget-aware Compression BibRef

Cho, S.[Sungmin], Kim, H.[Hyeseong], Kwon, J.[Junseok],
Filter Pruning Via Softmax Attention,
ICIP21(3507-3511)
IEEE DOI 2201
Image processing, Probabilistic logic, Softmax attention channel pruning, relative depth-wise separable convolutions BibRef

Dupont, R.[Robin], Sahbi, H.[Hichem], Michel, G.[Guillaume],
Weight Reparametrization for Budget-Aware Network Pruning,
ICIP21(789-793)
IEEE DOI 2201
Training, Degradation, Image processing, Task analysis, Standards, Videos, Lightweight network design, pruning, reparametrization BibRef

Boone-Sifuentes, T.[Tanya], Robles-Kelly, A.[Antonio], Nazari, A.[Asef],
Max-Variance Convolutional Neural Network Model Compression,
DICTA20(1-6)
IEEE DOI 2201
Couplings, Training, Image coding, Face recognition, Digital images, Filter banks, Convolutional neural networks, network pruning and max-variance pruning BibRef

Guerra, L.[Luis], Drummond, T.[Tom],
Automatic Pruning for Quantized Neural Networks,
DICTA21(01-08)
IEEE DOI 2201
Measurement, Quantization (signal), Image coding, Digital images, Neural networks, Euclidean distance BibRef

Jordão, A.[Artur], Pedrini, H.[Hélio],
On the Effect of Pruning on Adversarial Robustness,
AROW21(1-11)
IEEE DOI 2112
Training, Tools, Robustness, Computational efficiency BibRef

Lazarevich, I.[Ivan], Kozlov, A.[Alexander], Malinin, N.[Nikita],
Post-training deep neural network pruning via layer-wise calibration,
LPCV21(798-805)
IEEE DOI 2112
Deep learning, Computational modeling, Pipelines, Neural networks, Production, Object detection BibRef

Li, C.L.[Chang-Lin], Wang, G.R.[Guang-Run], Wang, B.[Bing], Liang, X.D.[Xiao-Dan], Li, Z.H.[Zhi-Hui], Chang, X.J.[Xiao-Jun],
Dynamic Slimmable Network,
CVPR21(8603-7613)
IEEE DOI 2111
Training, Image coding, Head, Computational modeling, Object detection, Life estimation, Logic gates BibRef

Yu, C.[Chong],
Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner,
CVPR21(3588-3597)
IEEE DOI 2111
Knowledge engineering, Minimally invasive surgery, Computational modeling, Throughput, Probability distribution, Task analysis BibRef

Gao, S.Q.[Shang-Qian], Huang, F.H.[Fei-Hu], Cai, W.D.[Wei-Dong], Huang, H.[Heng],
Network Pruning via Performance Maximization,
CVPR21(9266-9276)
IEEE DOI 2111
Training, Neural networks, Memory modules, Pattern recognition, Convolutional neural networks, Task analysis BibRef

Tang, Y.[Yehui], Wang, Y.H.[Yun-He], Xu, Y.X.[Yi-Xing], Deng, Y.P.[Yi-Ping], Xu, C.[Chao], Tao, D.C.[Da-Cheng], Xu, C.[Chang],
Manifold Regularized Dynamic Network Pruning,
CVPR21(5016-5026)
IEEE DOI 2111
Manifolds, Training, Degradation, Redundancy, Neural networks, Benchmark testing, Network architecture BibRef

Wang, Z.[Zi], Li, C.C.[Cheng-Cheng], Wang, X.Y.[Xiang-Yang],
Convolutional Neural Network Pruning with Structural Redundancy Reduction,
CVPR21(14908-14917)
IEEE DOI 2111
Image synthesis, Redundancy, Object detection, Network architecture, Pattern recognition BibRef

Yao, L.W.[Le-Wei], Pi, R.J.[Ren-Jie], Xu, H.[Hang], Zhang, W.[Wei], Li, Z.G.[Zhen-Guo], Zhang, T.[Tong],
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation,
CVPR21(10170-10179)
IEEE DOI 2111
Training, Costs, Heuristic algorithms, Detectors, Object detection, Search problems BibRef

Vemparala, M.R.[Manoj-Rohit], Fasfous, N.[Nael], Frickenstein, A.[Alexander], Sarkar, S.[Sreetama], Zhao, Q.[Qi], Kuhn, S.[Sabine], Frickenstein, L.[Lukas], Singh, A.[Anmol], Unger, C.[Christian], Nagaraja, N.S.[Naveen-Shankar], Wressnegger, C.[Christian], Stechele, W.[Walter],
Adversarial Robust Model Compression using In-Train Pruning,
SAIAD21(66-75)
IEEE DOI 2109
Training, Computational modeling, Robustness, Hardware, Pattern recognition BibRef

Jiang, W.[Wei], Wang, W.[Wei], Liu, S.[Shan], Li, S.[Songnan],
PnG: Micro-structured Prune-and-Grow Networks for Flexible Image Restoration,
NTIRE21(756-765)
IEEE DOI 2109
Degradation, Training, Image coding, Computational modeling, Superresolution, Image restoration BibRef

Enderich, L.[Lukas], Timm, F.[Fabian], Burgard, W.[Wolfram],
Holistic Filter Pruning for Efficient Deep Neural Networks,
WACV21(2595-2604)
IEEE DOI 2106
Training, Tensors, Computational modeling, Neural networks, Redundancy BibRef

Ganesh, M.R.[Madan Ravi], Corso, J.J.[Jason J.], Sekeh, S.Y.[Salimeh Yasaei],
MINT: Deep Network Compression via Mutual Information-based Neuron Trimming,
ICPR21(8251-8258)
IEEE DOI 2105
Sensitivity, Neurons, Redundancy, Filtering algorithms, Information filters, Robustness, Calibration BibRef

Joo, D.G.[Dong-Gyu], Kim, D.[Doyeon], Kim, J.[Junmo],
Slimming ResNet by Slimming Shortcut,
ICPR21(7677-7683)
IEEE DOI 2105
Convolution, Logic gates, Convolutional neural networks BibRef

Ferrari, C.[Claudio], Berretti, S.[Stefano], del Bimbo, A.[Alberto],
Probability Guided Maxout,
ICPR21(6517-6523)
IEEE DOI 2105
Training, Image classification BibRef

Yu, F.[Fang], Han, C.Q.[Chuan-Qi], Wang, P.C.[Peng-Cheng], Huang, R.[Ruoran], Huang, X.[Xi], Cui, L.[Li],
HFP: Hardware-Aware Filter Pruning for Deep Convolutional Neural Networks Acceleration,
ICPR21(255-262)
IEEE DOI 2105
Degradation, Training, Measurement, Information filters, Taylor series, Hardware, Pattern recognition BibRef

Shen, S.B.[Shi-Bo], Li, R.P.[Rong-Peng], Zhao, Z.F.[Zhi-Feng], Zhang, H.G.[Hong-Gang], Zhou, Y.[Yugeng],
Learning to Prune in Training via Dynamic Channel Propagation,
ICPR21(939-945)
IEEE DOI 2105
Training, Convolutional codes, Heuristic algorithms, Neural networks, Benchmark testing, Filtering algorithms, Pattern recognition BibRef

Nguyen-Meidine, L.T.[Le Thanh], Granger, E.[Eric], Kiran, M.[Madhu], Pedersoli, M.[Marco], Blais-Morin, L.A.[Louis-Antoine],
Progressive Gradient Pruning for Classification, Detection and Domain Adaptation,
ICPR21(2795-2802)
IEEE DOI 2105
Training, Backpropagation, Weight measurement, Visualization, Tensors, Object detection, Artificial neural networks BibRef

Mitsuno, K.[Kakeru], Kurita, T.[Takio],
Filter Pruning using Hierarchical Group Sparse Regularization for Deep Convolutional Neural Networks,
ICPR21(1089-1095)
IEEE DOI 2105
Training, Pattern recognition, Convolutional neural networks, Kernel BibRef

Soltani, M.[Mohammadreza], Wu, S.[Suya], Ding, J.[Jie], Ravier, R.[Robert], Tarokh, V.[Vahid],
On the Information of Feature Maps and Pruning of Deep Neural Networks,
ICPR21(6988-6995)
IEEE DOI 2105
Image coding, Simulation, Neural networks, Data models, Numerical models, Pattern recognition, Mutual information, feature maps BibRef

Foldy-Porto, T.[Timothy], Venkatesha, Y.[Yeshwanth], Panda, P.[Priyadarshini],
Activation Density Driven Efficient Pruning in Training,
ICPR21(8929-8936)
IEEE DOI 2105
Training, Neural networks, Real-time systems, Pattern recognition, Complexity theory, Deep Neural Networks BibRef

Zullich, M.[Marco], Medvet, E.[Eric], Pellegrino, F.A.[Felice Andrea], Ansuini, A.[Alessio],
Speeding-up pruning for Artificial Neural Networks: Introducing Accelerated Iterative Magnitude Pruning,
ICPR21(3868-3875)
IEEE DOI 2105
Training, Artificial neural networks, Pattern recognition, Iterative methods, Acceleration, Artificial Neural Network, Lottery Ticket Hypothesis BibRef

Huesmann, K.[Karim], Rodriguez, L.G.[Luis Garcia], Linsen, L.[Lars], Risse, B.[Benjamin],
The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks,
EDL-AI20(130-145).
Springer DOI 2103
BibRef

Abdiyeva, K.[Kamila], Lukac, M.[Martin], Ahuja, N.[Narendra],
Remove to Improve?,
EDL-AI20(146-161).
Springer DOI 2103
BibRef

Wimmer, P.[Paul], Mehnert, J.[Jens], Condurache, A.[Alexandru],
Freezenet: Full Performance by Reduced Storage Costs,
ACCV20(VI:685-701).
Springer DOI 2103
BibRef

He, J.J.[Jun-Jie], Chen, B.[Bohua], Ding, Y.Z.[Yin-Zhang], Li, D.X.[Dong-Xiao],
Feature Variance Ratio-guided Channel Pruning for Deep Convolutional Network Acceleration,
ACCV20(IV:170-186).
Springer DOI 2103
BibRef

Li, D.[Dong], Chen, S.[Sitong], Liu, X.D.[Xu-Dong], Sun, Y.[Yunda], Zhang, L.[Li],
Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed,
ACCV20(IV:252-267).
Springer DOI 2103
BibRef

Davoodikakhki, M.[Mahdi], Yin, K.[KangKang],
Hierarchical Action Classification with Network Pruning,
ISVC20(I:291-305).
Springer DOI 2103
BibRef

Elkerdawy, S.[Sara], Elhoushi, M.[Mostafa], Singh, A.[Abhineet], Zhang, H.[Hong], Ray, N.[Nilanjan],
To Filter Prune, or to Layer Prune, That Is the Question,
ACCV20(III:737-753).
Springer DOI 2103
BibRef

Duan, H.R.[Hao-Ran], Li, H.[Hui],
Channel Pruning for Accelerating Convolutional Neural Networks via Wasserstein Metric,
ACCV20(III:492-505).
Springer DOI 2103
BibRef

Xu, Z.W.[Zhi-Wei], Ajanthan, T.[Thalaiyasingam], Hartley, R.I.[Richard I.],
Fast and Differentiable Message Passing on Pairwise Markov Random Fields,
ACCV20(III:523-540).
Springer DOI 2103
BibRef

Xu, Z.W.[Zhi-Wei], Ajanthan, T.[Thalaiyasingam], Vineet, V., Hartley, R.I.[Richard I.],
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs,
3DV20(1-10)
IEEE DOI 2102
Neurons, Training, Memory management, Optimization, video classification BibRef

Ning, X.F.[Xue-Fei], Zhao, T.C.[Tian-Chen], Li, W.S.[Wen-Shuo], Lei, P.[Peng], Wang, Y.[Yu], Yang, H.Z.[Hua-Zhong],
DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation,
ECCV20(III:592-607).
Springer DOI 2012
BibRef

Ye, X.C.[Xu-Cheng], Dai, P.C.[Peng-Cheng], Luo, J.Y.[Jun-Yu], Guo, X.[Xin], Qi, Y.J.[Ying-Jie], Yang, J.L.[Jian-Lei], Chen, Y.R.[Yi-Ran],
Accelerating CNN Training by Pruning Activation Gradients,
ECCV20(XXV:322-338).
Springer DOI 2011
BibRef

Wang, Y.K.[Yi-Kai], Sun, F.C.[Fu-Chun], Li, D.[Duo], Yao, A.B.[An-Bang],
Resolution Switchable Networks for Runtime Efficient Image Recognition,
ECCV20(XV:533-549).
Springer DOI 2011
Code, Network Pruning.
WWW Link. Limit the network to vary image resolution and computation time. BibRef

Lee, M.K., Lee, S., Lee, S.H., Song, B.C.,
Channel Pruning Via Gradient Of Mutual Information For Light-Weight Convolutional Neural Networks,
ICIP20(1751-1755)
IEEE DOI 2011
Mutual information, Probability distribution, Random variables, Convolutional neural networks, Linear programming, Uncertainty, mutual information BibRef

Meyer, M., Wiesner, J., Rohlfing, C.,
Optimized Convolutional Neural Networks for Video Intra Prediction,
ICIP20(3334-3338)
IEEE DOI 2011
Training, Complexity theory, Encoding, Convolutional codes, Convolution, Kernel, video coding, pruning BibRef

Mousa-Pasandi, M., Hajabdollahi, M., Karimi, N., Samavi, S., Shirani, S.,
Convolutional Neural Network Pruning Using Filter Attenuation,
ICIP20(2905-2909)
IEEE DOI 2011
Attenuation, Filtering algorithms, Mathematical model, Computational modeling, Training, Convolutional neural networks, filter attenuation BibRef

Elkerdawy, S., Elhoushi, M., Singh, A., Zhang, H., Ray, N.,
One-Shot Layer-Wise Accuracy Approximation For Layer Pruning,
ICIP20(2940-2944)
IEEE DOI 2011
Computational modeling, Hardware, Training, Training data, Graphics processing units, Shape, Sensitivity analysis, inference speed up BibRef

Tian, H.D.[Hong-Duan], Liu, B.[Bo], Yuan, X.T.[Xiao-Tong], Liu, Q.S.[Qing-Shan],
Meta-learning with Network Pruning,
ECCV20(XIX:675-700).
Springer DOI 2011
BibRef

Li, Y.[Yawei], Gu, S.H.[Shu-Hang], Zhang, K.[Kai], Van Gool, L.J.[Luc J.], Timofte, R.[Radu],
DHP: Differentiable Meta Pruning via Hypernetworks,
ECCV20(VIII:608-624).
Springer DOI 2011
BibRef

Messikommer, N.[Nico], Gehrig, D.[Daniel], Loquercio, A.[Antonio], Scaramuzza, D.[Davide],
Event-based Asynchronous Sparse Convolutional Networks,
ECCV20(VIII:415-431).
Springer DOI 2011

WWW Link. Code, Semantic Segmentation.
WWW Link. Dataset, Semantic Segmentation. BibRef

Kim, B.[Byungjoo], Chudomelka, B.[Bryce], Park, J.[Jinyoung], Kang, J.[Jaewoo], Hong, Y.J.[Young-Joon], Kim, H.W.J.[Hyun-Woo J.],
Robust Neural Networks Inspired by Strong Stability Preserving Runge-Kutta Methods,
ECCV20(IX:416-432).
Springer DOI 2011
BibRef

Li, B.L.[Bai-Lin], Wu, B.[Bowen], Su, J.[Jiang], Wang, G.R.[Guang-Run],
Eagleeye: Fast Sub-net Evaluation for Efficient Neural Network Pruning,
ECCV20(II:639-654).
Springer DOI 2011
BibRef

Li, Y., Gu, S., Mayer, C., Van Gool, L.J., Timofte, R.,
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression,
CVPR20(8015-8024)
IEEE DOI 2008
Matrix decomposition, Convolution, Tensile stress, Fasteners, Matrix converters, Neural networks BibRef

Guo, S., Wang, Y., Li, Q., Yan, J.,
DMCP: Differentiable Markov Channel Pruning for Neural Networks,
CVPR20(1536-1544)
IEEE DOI 2008
Markov processes, Training, Task analysis, Mathematical model, Learning (artificial intelligence), Optimization BibRef

Lin, M., Ji, R., Wang, Y., Zhang, Y., Zhang, B., Tian, Y., Shao, L.,
HRank: Filter Pruning Using High-Rank Feature Map,
CVPR20(1526-1535)
IEEE DOI 2008
Acceleration, Training, Hardware, Adaptive systems, Optimization, Adaptation models, Neural networks BibRef

Luo, J., Wu, J.,
Neural Network Pruning With Residual-Connections and Limited-Data,
CVPR20(1455-1464)
IEEE DOI 2008
Training, Computational modeling, Neural networks, Data models, Image coding, Acceleration BibRef

Wu, Y., Liu, C., Chen, B., Chien, S.,
Constraint-Aware Importance Estimation for Global Filter Pruning under Multiple Resource Constraints,
EDLCV20(2935-2943)
IEEE DOI 2008
Estimation, Computational modeling, Training, Optimization, Performance evaluation, Taylor series BibRef

Gain, A.[Alex], Kaushik, P.[Prakhar], Siegelmann, H.[Hava],
Adaptive Neural Connections for Sparsity Learning,
WACV20(3177-3182)
IEEE DOI 2006
Training, Neurons, Bayes methods, Biological neural networks, Kernel, Computer science BibRef

Ramakrishnan, R.K., Sari, E., Nia, V.P.,
Differentiable Mask for Pruning Convolutional and Recurrent Networks,
CRV20(222-229)
IEEE DOI 2006
BibRef

Blakeney, C., Yan, Y., Zong, Z.,
Is Pruning Compression?: Investigating Pruning Via Network Layer Similarity,
WACV20(903-911)
IEEE DOI 2006
Biological neural networks, Neurons, Correlation, Computational modeling, Training, Tools BibRef

Verma, V.K., Singh, P., Namboodiri, V.P., Rai, P.,
A 'Network Pruning Network' Approach to Deep Model Compression,
WACV20(2998-3007)
IEEE DOI 2006
Computational modeling, Task analysis, Adaptation models, Cost function, Computer science, Iterative methods BibRef

Gao, S., Liu, X., Chien, L., Zhang, W., Alvarez, J.M.,
VACL: Variance-Aware Cross-Layer Regularization for Pruning Deep Residual Networks,
CEFRL19(2980-2988)
IEEE DOI 2004
image filtering, neural nets, statistical analysis, CIFAR10, first-order statistics, second-order statistics, residual networks BibRef

Liu, Z., Mu, H., Zhang, X., Guo, Z., Yang, X., Cheng, K., Sun, J.,
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning,
ICCV19(3295-3304)
IEEE DOI 2004
Code, Neural Networks.
WWW Link. learning (artificial intelligence), neural nets, sampling methods, stochastic processes, pruned networks, Task analysis BibRef

Zhou, Y., Zhang, Y., Wang, Y., Tian, Q.,
Accelerate CNN via Recursive Bayesian Pruning,
ICCV19(3305-3314)
IEEE DOI 2004
approximation theory, Bayes methods, computational complexity, convolutional neural nets, Markov processes, Computational modeling BibRef

Molchanov, P.[Pavlo], Mallya, A.[Arun], Tyree, S.[Stephen], Frosio, I.[Iuri], Kautz, J.[Jan],
Importance Estimation for Neural Network Pruning,
CVPR19(11256-11264).
IEEE DOI 2002
BibRef

Webster, R.[Ryan], Rabin, J.[Julien], Simon, L.[Loic], Jurie, F.[Frederic],
Detecting Overfitting of Deep Generative Networks via Latent Recovery,
CVPR19(11265-11274).
IEEE DOI 2002
BibRef

Lemaire, C.[Carl], Achkar, A.[Andrew], Jodoin, P.M.[Pierre-Marc],
Structured Pruning of Neural Networks With Budget-Aware Regularization,
CVPR19(9100-9108).
IEEE DOI 2002
BibRef

Ding, X.O.[Xia-Ohan], Ding, G.G.[Gui-Guang], Guo, Y.C.[Yu-Chen], Han, J.G.[Jun-Gong],
Centripetal SGD for Pruning Very Deep Convolutional Networks With Complicated Structure,
CVPR19(4938-4948).
IEEE DOI 2002
BibRef

He, Y.[Yang], Ding, Y.H.[Yu-Hang], Liu, P.[Ping], Zhu, L.C.[Lin-Chao], Zhang, H.W.[Han-Wang], Yang, Y.[Yi],
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration,
CVPR20(2006-2015)
IEEE DOI 2008
Acceleration, Feature extraction, Training, Convolutional neural networks, Benchmark testing, Computer architecture BibRef

He, Y.[Yang], Liu, P.[Ping], Wang, Z.W.[Zi-Wei], Hu, Z.L.[Zhi-Lan], Yang, Y.[Yi],
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration,
CVPR19(4335-4344).
IEEE DOI 2002
BibRef

Zhao, C.L.[Cheng-Long], Ni, B.B.[Bing-Bing], Zhang, J.[Jian], Zhao, Q.[Qiwei], Zhang, W.J.[Wen-Jun], Tian, Q.[Qi],
Variational Convolutional Neural Network Pruning,
CVPR19(2775-2784).
IEEE DOI 2002
BibRef

Lin, S.H.[Shao-Hui], Ji, R.R.[Rong-Rong], Yan, C.Q.[Chen-Qian], Zhang, B.C.[Bao-Chang], Cao, L.J.[Liu-Juan], Ye, Q.X.[Qi-Xiang], Huang, F.Y.[Fei-Yue], Doermann, D.[David],
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning,
CVPR19(2785-2794).
IEEE DOI 2002
BibRef

Mummadi, C.K.[Chaithanya Kumar], Genewein, T.[Tim], Zhang, D.[Dan], Brox, T.[Thomas], Fischer, V.[Volker],
Group Pruning Using a Bounded-Lp Norm for Group Gating and Regularization,
GCPR19(139-155).
Springer DOI 1911
BibRef

Wang, W.T.[Wei-Ting], Li, H.L.[Han-Lin], Lin, W.S.[Wei-Shiang], Chiang, C.M.[Cheng-Ming], Tsai, Y.M.[Yi-Min],
Architecture-Aware Network Pruning for Vision Quality Applications,
ICIP19(2701-2705)
IEEE DOI 1910
Pruning, Vision Quality, Network Architecture BibRef

Zhang, Y.X.[Yu-Xin], Wang, H.A.[Hu-An], Luo, Y.[Yang], Yu, L.[Lu], Hu, H.J.[Hao-Ji], Shan, H.G.[Hang-Guan], Quek, T.Q.S.[Tony Q. S.],
Three-Dimensional Convolutional Neural Network Pruning with Regularization-Based Method,
ICIP19(4270-4274)
IEEE DOI 1910
3D CNN, video analysis, model compression, structured pruning, regularization BibRef

Hu, Y., Sun, S., Li, J., Zhu, J., Wang, X., Gu, Q.,
Multi-Loss-Aware Channel Pruning of Deep Networks,
ICIP19(889-893)
IEEE DOI 1910
deep neural networks, object classification, model compression, channel pruning BibRef

Yu, R., Li, A., Chen, C., Lai, J., Morariu, V.I., Han, X., Gao, M., Lin, C., Davis, L.S.,
NISP: Pruning Networks Using Neuron Importance Score Propagation,
CVPR18(9194-9203)
IEEE DOI 1812
Neurons, Redundancy, Optimization, Acceleration, Biological neural networks, Task analysis, Feature extraction BibRef

Zhang, T.[Tianyun], Ye, S.[Shaokai], Zhang, K.Q.[Kai-Qi], Tang, J.[Jian], Wen, W.[Wujie], Fardad, M.[Makan], Wang, Y.Z.[Yan-Zhi],
A Systematic DNN Weight Pruning Framework Using Alternating Direction Method of Multipliers,
ECCV18(VIII: 191-207).
Springer DOI 1810
BibRef

Huang, Q., Zhou, K., You, S., Neumann, U.,
Learning to Prune Filters in Convolutional Neural Networks,
WACV18(709-718)
IEEE DOI 1806
image segmentation, learning (artificial intelligence), neural nets, CNN filters, Training BibRef

Carreira-Perpinan, M.A., Idelbayev, Y.,
'Learning-Compression' Algorithms for Neural Net Pruning,
CVPR18(8532-8541)
IEEE DOI 1812
Neural networks, Optimization, Training, Neurons, Performance evaluation, Mobile handsets, Quantization (signal) BibRef

Zhou, Z., Zhou, W., Li, H., Hong, R.,
Online Filter Clustering and Pruning for Efficient Convnets,
ICIP18(11-15)
IEEE DOI 1809
Training, Acceleration, Neural networks, Convolution, Tensile stress, Force, Clustering algorithms, Deep neural networks, similar filter, cluster loss BibRef

Zhu, L.G.[Li-Geng], Deng, R.Z.[Rui-Zhi], Maire, M.[Michael], Deng, Z.W.[Zhi-Wei], Mori, G.[Greg], Tan, P.[Ping],
Sparsely Aggregated Convolutional Networks,
ECCV18(XII: 192-208).
Springer DOI 1810
BibRef

Wang, Z., Zhu, C., Xia, Z., Guo, Q., Liu, Y.,
Towards thinner convolutional neural networks through gradually global pruning,
ICIP17(3939-3943)
IEEE DOI 1803
Computational modeling, Machine learning, Measurement, Neurons, Redundancy, Tensile stress, Training, Artificial neural networks, Deep learning BibRef

Luo, J.H., Wu, J., Lin, W.,
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression,
ICCV17(5068-5076)
IEEE DOI 1802
data compression, image coding, image filtering, inference mechanisms, neural nets, optimisation, Training BibRef

Rueda, F.M.[Fernando Moya], Grzeszick, R.[Rene], Fink, G.A.[Gernot A.],
Neuron Pruning for Compressing Deep Networks Using Maxout Architectures,
GCPR17(177-188).
Springer DOI 1711
BibRef

Yang, T.J.[Tien-Ju], Chen, Y.H.[Yu-Hsin], Sze, V.[Vivienne],
Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning,
CVPR17(6071-6079)
IEEE DOI 1711
Computational modeling, Energy consumption, Estimation, Hardware, Measurement, Memory management, Smart, phones BibRef

Guo, J.[Jia], Potkonjak, M.[Miodrag],
Pruning ConvNets Online for Efficient Specialist Models,
ECVW17(430-437)
IEEE DOI 1709
Biological neural networks, Computational modeling, Convolution, Memory management, Sensitivity, analysis BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Net Compression .