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, Computer architecture,
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.[Luoqian],
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, Computer architecture, 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
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.[Chenghao],
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.[Weihan],
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, Computer architecture,
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, Computer architecture,
Standards, Efficient training and inference methods,
BibRef
Zhan, Z.[Zheng],
Gong, Y.[Yifan],
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, Computer architecture, Euclidean distance
BibRef
Jordão, A.[Artur],
Pedrini, H.[Hélio],
On the Effect of Pruning on Adversarial Robustness,
AROW21(1-11)
IEEE DOI
2112
Training, Computer architecture, 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,
Computer architecture, Network architecture, Pattern recognition
BibRef
Yao, L.[Lewei],
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,
Computer architecture, 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, Computer architecture,
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, Computer architecture, 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
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, Computer architecture, 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,
Computer architecture, 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 architecture, 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 .