14.5.7.5.3 Neural Net Pruning

Chapter Contents (Back)
CNN. Efficient Implementation.

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


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

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 .


Last update:Mar 29, 2020 at 12:14:19