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
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
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 architecture, Computer science, Iterative methods
BibRef
Xiong, Y.,
Mehta, R.,
Singh, V.,
Resource Constrained Neural Network Architecture Search:
Will a Submodularity Assumption Help?,
ICCV19(1901-1910)
IEEE DOI
2004
learning (artificial intelligence), neural nets, optimisation,
neural network architecture search, empirical feedback,
Heuristic algorithms
BibRef
Dong, X.Y.[Xuan-Yi],
Yang, Y.[Yi],
One-Shot Neural Architecture Search via Self-Evaluated Template
Network,
ICCV19(3680-3689)
IEEE DOI
2004
image sampling, knowledge based systems,
learning (artificial intelligence), neural net architecture,
BibRef
Ajanthan, T.,
Dokania, P.,
Hartley, R.,
Torr, P.,
Proximal Mean-Field for Neural Network Quantization,
ICCV19(4870-4879)
IEEE DOI
2004
computational complexity, gradient methods, neural nets,
optimisation, stochastic processes, proximal mean-field, Labeling
BibRef
Gong, R.,
Liu, X.,
Jiang, S.,
Li, T.,
Hu, P.,
Lin, J.,
Yu, F.,
Yan, J.,
Differentiable Soft Quantization:
Bridging Full-Precision and Low-Bit Neural Networks,
ICCV19(4851-4860)
IEEE DOI
2004
backpropagation, convolutional neural nets, data compression,
image coding, learning (artificial intelligence), Backpropagation
BibRef
Heo, B.[Byeongho],
Kim, J.[Jeesoo],
Yun, S.[Sangdoo],
Park, H.[Hyojin],
Kwak, N.[Nojun],
Choi, J.Y.[Jin Young],
A Comprehensive Overhaul of Feature Distillation,
ICCV19(1921-1930)
IEEE DOI
2004
feature extraction, image classification, image segmentation,
object detection, distillation loss,
Artificial intelligence
BibRef
Mullapudi, R.T.,
Chen, S.,
Zhang, K.,
Ramanan, D.,
Fatahalian, K.,
Online Model Distillation for Efficient Video Inference,
ICCV19(3572-3581)
IEEE DOI
2004
convolutional neural nets, image segmentation,
inference mechanisms, learning (artificial intelligence),
Cameras
BibRef
Zhang, L.,
Song, J.,
Gao, A.,
Chen, J.,
Bao, C.,
Ma, K.,
Be Your Own Teacher: Improve the Performance of Convolutional Neural
Networks via Self Distillation,
ICCV19(3712-3721)
IEEE DOI
2004
convolutional neural nets, learning (artificial intelligence),
knowledge distillation, student neural networks,
Computational modeling
BibRef
Cho, J.H.,
Hariharan, B.,
On the Efficacy of Knowledge Distillation,
ICCV19(4793-4801)
IEEE DOI
2004
learning (artificial intelligence), neural nets, Probability distribution,
teacher architectures, knowledge distillation performance.
BibRef
Peng, B.,
Jin, X.,
Li, D.,
Zhou, S.,
Wu, Y.,
Liu, J.,
Zhang, Z.,
Liu, Y.,
Correlation Congruence for Knowledge Distillation,
ICCV19(5006-5015)
IEEE DOI
2004
correlation methods, face recognition, image classification,
learning (artificial intelligence), instance-level information,
Knowledge transfer
BibRef
Yu, J.,
Huang, T.,
Universally Slimmable Networks and Improved Training Techniques,
ICCV19(1803-1811)
IEEE DOI
2004
Code, Neural Networks.
WWW Link. image classification, image resolution,
learning (artificial intelligence), mobile computing,
Testing
BibRef
Tung, F.[Fred],
Mori, G.[Greg],
Similarity-Preserving Knowledge Distillation,
ICCV19(1365-1374)
IEEE DOI
2004
learning (artificial intelligence), neural nets,
semantic networks, Task analysis
BibRef
Jin, X.[Xiao],
Peng, B.Y.[Bao-Yun],
Wu, Y.C.[Yi-Chao],
Liu, Y.[Yu],
Liu, J.H.[Jia-Heng],
Liang, D.[Ding],
Yan, J.J.[Jun-Jie],
Hu, X.L.[Xiao-Lin],
Knowledge Distillation via Route Constrained Optimization,
ICCV19(1345-1354)
IEEE DOI
2004
face recognition, image classification,
learning (artificial intelligence), neural nets, optimisation,
Neural networks
BibRef
Choukroun, Y.,
Kravchik, E.,
Yang, F.,
Kisilev, P.,
Low-bit Quantization of Neural Networks for Efficient Inference,
CEFRL19(3009-3018)
IEEE DOI
2004
inference mechanisms, learning (artificial intelligence),
mean square error methods, neural nets, quantisation (signal), MMSE
BibRef
Zhao, R.,
Luk, W.,
Efficient Structured Pruning and Architecture Searching for Group
Convolution,
NeruArch19(1961-1970)
IEEE DOI
2004
convolutional neural nets, group theory, network theory (graphs),
neural net architecture, search problems, network pruning,
efficient inference
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,
computer vision, 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
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
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,
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 .