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
Tung, F.[Frederick],
Mori, G.[Greg],
Deep Neural Network Compression by In-Parallel Pruning-Quantization,
PAMI(42), No. 3, March 2020, pp. 568-579.
IEEE DOI
2002
BibRef
Earlier:
CLIP-Q: Deep Network Compression Learning by In-parallel
Pruning-Quantization,
CVPR18(7873-7882)
IEEE DOI
1812
Quantization (signal), Image coding, Neural networks,
Visualization, Training, Convolution, Network architecture,
Bayesian optimization.
Training, Task analysis, Optimization
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
Li, X.[Xin],
Zhou, Y.M.[Yi-Ming],
Pan, Z.[Zheng],
Feng, J.[Jiashi],
Partial Order Pruning: For Best Speed/Accuracy Trade-Off in Neural
Architecture Search,
CVPR19(9137-9145).
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.[Xiaohan],
Ding, G.[Guiguang],
Guo, Y.[Yuchen],
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],
Liu, P.[Ping],
Wang, Z.[Ziwei],
Hu, Z.[Zhilan],
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.,
Wang, H.,
Luo, Y.,
Yu, L.,
Hu, H.,
Shan, H.,
Quek, T.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.,
Li, J.,
Long, X.,
Hu, S.,
Zhu, J.,
Wang, X.,
Gu, Q.,
Cluster Regularized Quantization for Deep Networks Compression,
ICIP19(914-918)
IEEE DOI
1910
deep neural networks, object classification, model compression, quantization
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
Manessi, F.,
Rozza, A.,
Bianco, S.,
Napoletano, P.,
Schettini, R.,
Automated Pruning for Deep Neural Network Compression,
ICPR18(657-664)
IEEE DOI
1812
Training, Neural networks, Quantization (signal), Task analysis,
Feature extraction, Pipelines, Image coding
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
computer vision, 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
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,
Computer vision, Convolution, Memory management, Sensitivity, analysis
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Forgetting, Explaination, Intrepretation, Understanding of Convolutional Neural Networks .