14.5.9.8.9 Neural Net Compression

Chapter Contents (Back)
CNN. Compression. Efficient Implementation.
See also Neural Net Pruning.
See also Knowledge Distillation.
See also Neural Net Quantization.

Wang, W.[Wei], Zhu, L.Q.[Li-Qiang],
Structured feature sparsity training for convolutional neural network compression,
JVCIR(71), 2020, pp. 102867.
Elsevier DOI 2009
Convolutional neural network, CNN compression, Structured sparsity, Pruning criterion BibRef

Kaplan, C.[Cagri], Bulbul, A.[Abdullah],
Goal driven network pruning for object recognition,
PR(110), 2021, pp. 107468.
Elsevier DOI 2011
Deep learning, Network pruning, Network compressing, Top-down attention, Perceptual visioning BibRef

Yao, K.X.[Kai-Xuan], Cao, F.L.[Fei-Long], Leung, Y.[Yee], Liang, J.[Jiye],
Deep neural network compression through interpretability-based filter pruning,
PR(119), 2021, pp. 108056.
Elsevier DOI 2106
Deep neural network (DNN), Convolutional neural network (CNN), Visualization, Compression BibRef

Gowdra, N.[Nidhi], Sinha, R.[Roopak], MacDonell, S.[Stephen], Yan, W.Q.[Wei Qi],
Mitigating severe over-parameterization in deep convolutional neural networks through forced feature abstraction and compression with an entropy-based heuristic,
PR(119), 2021, pp. 108057.
Elsevier DOI 2106
Convolutional neural networks (CNNs), Depth redundancy, Entropy, Feature compression, EBCLE BibRef

Zhang, H.J.[Hui-Jie], An, L.[Li], Chu, V.W.[Vena W.], Stow, D.A.[Douglas A.], Liu, X.B.[Xiao-Bai], Ding, Q.H.[Qing-Hua],
Learning Adjustable Reduced Downsampling Network for Small Object Detection in Urban Environments,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Yao, J.[Jie], Wang, D.D.[Dong-Dong], Hu, H.[Hao], Xing, W.W.[Wei-Wei], Wang, L.Q.[Li-Qiang],
ADCNN: Towards learning adaptive dilation for convolutional neural networks,
PR(123), 2022, pp. 108369.
Elsevier DOI 2112
Adaptive dilated convolution, Representation learning, Image classification BibRef

Tahiri, Y.[Younes], Seddik, M.E.[Mohamed El_Amine], Tamaazousti, M.[Mohamed],
Optimization-Based Neural Networks Compression,
ICIP21(3512-3516)
IEEE DOI 2201
Performance evaluation, Image coding, Neurons, Memory management, Task analysis, Biological neural networks, Distillation BibRef

Rueckauer, B.[Bodo], Liu, S.C.[Shih-Chii],
Contraction of Dynamically Masked Deep Neural Networks for Efficient Video Processing,
CirSysVideo(32), No. 2, February 2022, pp. 621-633.
IEEE DOI 2202
Neurons, Taylor series, Surveillance, Sparse matrices, Heuristic algorithms, Correlation, Biological neural networks, masking BibRef

Wang, Z.Z.[Zhen-Zhen], Qin, M.H.[Ming-Hai], Chen, Y.K.[Yen-Kuang],
Learning from the CNN-based Compressed Domain,
WACV22(4000-4008)
IEEE DOI 2202
Training, Image segmentation, Image coding, Computational modeling, Estimation, Transform coding, Entropy, Semi- and Un- supervised Learning BibRef

Kirchhoffer, H.[Heiner], Haase, P.[Paul], Samek, W.[Wojciech], Müller, K.[Karsten], Rezazadegan-Tavakoli, H.[Hamed], Cricri, F.[Francesco], Aksu, E.B.[Emre B.], Hannuksela, M.M.[Miska M.], Jiang, W.[Wei], Wang, W.[Wei], Liu, S.[Shan], Jain, S.[Swayambhoo], Hamidi-Rad, S.[Shahab], Racapé, F.[Fabien], Bailer, W.[Werner],
Overview of the Neural Network Compression and Representation (NNR) Standard,
CirSysVideo(32), No. 5, May 2022, pp. 3203-3216.
IEEE DOI 2205
Artificial neural networks, Quantization (signal), Biological neural networks, Standards, Tensors, Decoding, Training, machine learning BibRef

Ji, Y.W.[Yu-Wang], Wang, Q.[Qiang],
Fast CP-compression layer: Tensor CP-decomposition to compress layers in deep learning,
IET-IPR(16), No. 9, 2022, pp. 2535-2543.
DOI Link 2206
tensor Canonical Polyadic. BibRef

Zhang, L.F.[Lin-Feng], Bao, C.L.[Cheng-Long], Ma, K.S.[Kai-Sheng],
Self-Distillation: Towards Efficient and Compact Neural Networks,
PAMI(44), No. 8, August 2022, pp. 4388-4403.
IEEE DOI 2207
Neural networks, Knowledge engineering, Training, Computational modeling, Acceleration, image classification BibRef

Young, S.I.[Sean I.], Zhe, W.[Wang], Taubman, D.[David], Girod, B.[Bernd],
Transform Quantization for CNN Compression,
PAMI(44), No. 9, September 2022, pp. 5700-5714.
IEEE DOI 2208
Quantization (signal), Transforms, Kernel, Decorrelation, Convolution, Training, Image coding, Convolutional neural networks, learned transforms BibRef

Mo, R.Y.[Rong-Yun], Lai, S.Q.[Shen-Qi], Yan, Y.[Yan], Chai, Z.H.[Zhen-Hua], Wei, X.L.[Xiao-Lin],
Dimension-aware attention for efficient mobile networks,
PR(131), 2022, pp. 108899.
Elsevier DOI 2208
Efficient mobile networks, Attention mechanism, Feature enhancement, Multi-branch factorization, Multi-dimensional information BibRef

Yan, T.W.[Tian-Wei], Zhang, N.[Ning], Li, J.[Jie], Liu, W.C.[Wen-Chao], Chen, H.[He],
Automatic Deployment of Convolutional Neural Networks on FPGA for Spaceborne Remote Sensing Application,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Liu, Y.F.[Yu-Fan], Cao, J.J.[Jia-Jiong], Li, B.[Bing], Hu, W.M.[Wei-Ming], Maybank, S.[Stephen],
Learning to Explore Distillability and Sparsability: A Joint Framework for Model Compression,
PAMI(45), No. 3, March 2023, pp. 3378-3395.
IEEE DOI 2302
Training, Optimization, Knowledge engineering, Computational modeling, Analytical models, Heuristic algorithms, deep learning BibRef

Tian, Q.[Qing], Arbel, T.[Tal], Clark, J.J.[James J.],
Grow-push-prune: Aligning deep discriminants for effective structural network compression,
CVIU(231), 2023, pp. 103682.
Elsevier DOI 2305
Deep neural network pruning, Deep discriminant analysis, Deep representation learning BibRef

Guo, S.[Suhan], Lai, B.L.[Bi-Lan], Yang, S.[Suorong], Zhao, J.[Jian], Shen, F.[Furao],
Sensitivity pruner: Filter-Level compression algorithm for deep neural networks,
PR(140), 2023, pp. 109508.
Elsevier DOI 2305
Filter pruning, Saliency-based pruning, End-to-end pruning framework, Sampling bias BibRef

Zhu, Y.Y.[Yang-Yang], Xie, L.[Luofeng], Xie, Z.[Zhengfeng], Yin, M.[Ming], Yin, G.[Guofu],
FSConv: Flexible and separable convolution for convolutional neural networks compression,
PR(140), 2023, pp. 109589.
Elsevier DOI 2305
CNNs compression, Representative feature maps, Redundant feature maps, Intrinsic information, Tiny hidden details BibRef

Lu, W.Z.[Wei-Zhi], Chen, M.[Mingrui], Guo, K.[Kai], Li, W.Y.[Wei-Yu],
Cascaded Compressed Sensing Networks,
SPLetters(30), 2023, pp. 364-368.
IEEE DOI 2305
Compressed sensing, Transforms, Sensors, Dictionaries, Sparse matrices, Machine learning, Complexity theory, sparse transform BibRef

Guo, L.[Lie], Zhao, Y.B.[Yi-Bing], Gao, J.D.[Jian-Dong],
Compression of Vehicle and Pedestrian Detection Network Based on YOLOv3 Model,
IEICE(E106-D), No. 5, May 2023, pp. 735-745.
WWW Link. 2305
BibRef

Peng, P.[Peng], You, M.Y.[Ming-Yu], Jiang, K.[Kai], Lian, Y.[Youzao], Xu, W.S.[Wei-Sheng],
MBFQuant: A Multiplier-Bitwidth-Fixed, Mixed-Precision Quantization Method for Mobile CNN-Based Applications,
IP(32), 2023, pp. 2438-2453.
IEEE DOI 2305
Quantization (signal), Tensors, Convolutional neural networks, Heuristic algorithms, Hardware, Simulated annealing, model compression BibRef

Zhang, C.Y.[Chao-Yan], Li, C.[Cheng], Guo, B.[Baolong], Liao, N.N.[Nan-Nan],
Neural Network Compression via Low Frequency Preference,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Wu, J.[Jie], Zhu, D.[Dingshun], Fang, L.Y.[Le-Yuan], Deng, Y.[Yue], Zhong, Z.[Zhun],
Efficient Layer Compression Without Pruning,
IP(32), 2023, pp. 4689-4700.
IEEE DOI 2309
BibRef

Chen, J.[Jun], Bai, S.P.[Shi-Peng], Huang, T.X.[Tian-Xin], Wang, M.M.[Meng-Meng], Tian, G.Z.[Guan-Zhong], Liu, Y.[Yong],
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning,
PR(143), 2023, pp. 109780.
Elsevier DOI 2310
Neural network compression, Date-free quantization BibRef

Bai, S.P.[Shi-Peng], Chen, J.[Jun], Shen, X.[Xintian], Qian, Y.X.[Yi-Xuan], Liu, Y.[Yong],
Unified Data-Free Compression: Pruning and Quantization without Fine-Tuning,
ICCV23(5853-5862)
IEEE DOI 2401
BibRef

Duan, W.H.[Wen-Hong], Liu, Z.H.[Zhen-Hua], Jia, C.M.[Chuan-Min], Wang, S.S.[Shan-She], Ma, S.W.[Si-Wei], Gao, W.[Wen],
Differential Weight Quantization for Multi-Model Compression,
MultMed(25), 2023, pp. 6397-6410.
IEEE DOI 2311
quantization in deep network BibRef

Lan, W.C.[Wei-Chao], Cheung, Y.M.[Yiu-Ming], Jiang, J.[Juyong], Hu, Z.[Zhikai], Li, M.[Mengke],
Compact Neural Network via Stacking Hybrid Units,
PAMI(46), No. 1, January 2024, pp. 103-116.
IEEE DOI 2312
BibRef

Bai, S.P.[Shi-Peng], Chen, J.[Jun], Yang, Y.[Yu], Liu, Y.[Yong],
Multi-Dimension Compression of Feed-Forward Network in Vision Transformers,
PRL(176), 2023, pp. 56-61.
Elsevier DOI 2312
Vision Transformers, Feed-Forward Network, Pruning, FLOPs, Parameters BibRef

Wang, Z.Y.[Zhen-Yu], Xie, X.M.[Xue-Mei], Zhao, Q.[Qinghang], Shi, G.M.[Guang-Ming],
Filter Clustering for Compressing CNN Model With Better Feature Diversity,
CirSysVideo(33), No. 12, December 2023, pp. 7385-7397.
IEEE DOI 2312
BibRef

Tan, Q.F.[Qi-Fan], Yang, X.[Xuqi], Qiu, C.[Cheng], Jiang, Y.[Yanhuan], He, J.Z.[Jin-Ze], Liu, J.[Jingshuo], Wu, Y.H.[Ya-Hui],
SCCMDet: Adaptive Sparse Convolutional Networks Based on Class Maps for Real-Time Onboard Detection in Unmanned Aerial Vehicle Remote Sensing Images,
RS(16), No. 6, 2024, pp. 1031.
DOI Link 2403
BibRef


van Betteray, A.[Antonia], Rottmann, M.[Matthias], Kahl, K.[Karsten],
MGiaD: Multigrid in all dimensions. Efficiency and robustness by weight sharing and coarsening in resolution and channel dimensions*,
REDLCV23(1284-1293)
IEEE DOI 2401
BibRef

Vo, Q.H.[Quang Hieu], Tran, L.T.[Linh-Tam], Bae, S.H.[Sung-Ho], Kim, L.W.[Lok-Won], Hong, C.S.[Choong Seon],
MST-compression: Compressing and Accelerating Binary Neural Networks with Minimum Spanning Tree,
ICCV23(6068-6077)
IEEE DOI 2401
BibRef

Shi, Y.[Yumeng], Bai, S.H.[Shi-Hao], Wei, X.[Xiuying], Gong, R.[Ruihao], Yang, J.[Jianlei],
Lossy and Lossless (L2) Post-training Model Size Compression,
ICCV23(17500-17510)
IEEE DOI Code:
WWW Link. 2401
BibRef

Nooralinejad, P.[Parsa], Abbasi, A.[Ali], Koohpayegani, S.A.[Soroush Abbasi], Meibodi, K.P.[Kossar Pourahmadi], Khan, R.M.S.[Rana Muhammad Shahroz], Kolouri, S.[Soheil], Pirsiavash, H.[Hamed],
PRANC: Pseudo RAndom Networks for Compacting deep models,
ICCV23(16975-16985)
IEEE DOI Code:
WWW Link. 2401
BibRef

Xu, K.X.[Kai-Xin], Lee, A.H.X.[Alina Hui Xiu], Zhao, Z.Y.[Zi-Yuan], Wang, Z.[Zhe], Wu, M.[Min], Lin, W.S.[Wei-Si],
Metagrad: Adaptive Gradient Quantization with Hypernetworks,
ICIP23(276-280)
IEEE DOI 2312
BibRef

Hesse, R.[Robin], Schaub-Meyer, S.[Simone], Roth, S.[Stefan],
Content-Adaptive Downsampling in Convolutional Neural Networks,
ECV23(4544-4553)
IEEE DOI 2309
BibRef

Hu, T.[Tie], Lin, M.[Mingbao], You, L.[Lizhou], Chao, F.[Fei], Ji, R.R.[Rong-Rong],
Discriminator-Cooperated Feature Map Distillation for GAN Compression,
CVPR23(20351-20360)
IEEE DOI 2309
BibRef

Koryakovskiy, I.[Ivan], Yakovleva, A.[Alexandra], Buchnev, V.[Valentin], Isaev, T.[Temur], Odinokikh, G.[Gleb],
One-Shot Model for Mixed-Precision Quantization,
CVPR23(7939-7949)
IEEE DOI 2309
BibRef

Ma, Y.X.[Yue-Xiao], Li, H.X.[Hui-Xia], Zheng, X.[Xiawu], Xiao, X.F.[Xue-Feng], Wang, R.[Rui], Wen, S.L.[Shi-Lei], Pan, X.[Xin], Chao, F.[Fei], Ji, R.R.[Rong-Rong],
Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective,
CVPR23(7950-7959)
IEEE DOI 2309
BibRef

Manjah, D.[Dani], Cacciarelli, D.[Davide], Benkedadra, M.[Mohamed], Standaert, B.[Baptiste], de Hertaing, G.R.[Gauthier Rotsart], Macq, B.[Benoît], Galland, S.[Stéphane], de Vleeschouwer, C.[Christophe],
Stream-Based Active Distillation for Scalable Model Deployment,
L3D-IVU23(4999-5007)
IEEE DOI 2309
BibRef

Xu, X.W.[Xiu-Wei], Wang, Z.W.[Zi-Wei], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis,
CVPR23(5313-5322)
IEEE DOI 2309
BibRef

Lin, C.[Chen], Peng, B.[Bo], Li, Z.[Zheyang], Tan, W.M.[Wen-Ming], Ren, Y.[Ye], Xiao, J.[Jun], Pu, S.L.[Shi-Liang],
Bit-shrinking: Limiting Instantaneous Sharpness for Improving Post-training Quantization,
CVPR23(16196-16205)
IEEE DOI 2309
BibRef

Cai, J.X.[Jia-Xuan], Qi, Z.[Zhi], Fu, K.Q.[Ke-Qi], Shi, X.[Xulong], Li, Z.[Zan], Liu, X.Y.[Xuan-Yu], Liu, H.[Hao],
Pbcstereo: A Compressed Stereo Network with Pure Binary Convolutional Operations,
ACCV22(III:626-641).
Springer DOI 2307
BibRef

Kim, S.[Soyeong], Kim, D.Y.[Do-Yeon], Moon, J.[Jaekyun],
Deep Neural Network Compression for Image Inpainting,
CADK22(99-114).
Springer DOI 2304
BibRef

Given, N.A.[No Author],
LCS: Learning Compressible Subspaces for Efficient, Adaptive, Real-Time Network Compression at Inference Time,
WACV23(3807-3816)
IEEE DOI 2302
Portable document format, Applications: Smartphones/end user devices BibRef

Gordon, C.[Cameron], Chng, S.F.[Shin-Fang], MacDonald, L.[Lachlan], Lucey, S.[Simon],
On Quantizing Implicit Neural Representations,
WACV23(341-350)
IEEE DOI 2302
Training, Quantization (signal), Image coding, Computational modeling, Neural networks BibRef

Pham, C.[Cuong], Hoang, T.[Tuan], Do, T.T.[Thanh-Toan],
Collaborative Multi-Teacher Knowledge Distillation for Learning Low Bit-width Deep Neural Networks,
WACV23(6424-6432)
IEEE DOI 2302
Knowledge engineering, Deep learning, Training, Quantization (signal), Federated learning, Embedded sensing/real-time techniques BibRef

Horton, M.[Maxwell], Jin, Y.Z.[Yan-Zi], Farhadi, A.[Ali], Rastegari, M.[Mohammad],
Layer-Wise Data-Free CNN Compression,
ICPR22(2019-2026)
IEEE DOI 2212
Quantization (signal), Image coding, Neural networks, Training data, Computational efficiency BibRef

Andreev, P.[Pavel], Fritzler, A.[Alexander],
Quantization of Generative Adversarial Networks for Efficient Inference: A Methodological Study,
ICPR22(2179-2185)
IEEE DOI 2212
Performance evaluation, Training, Quantization (signal), Computational modeling, Semantics, Neural network compression BibRef

Liu, Y.C.[Yu-Chen], Wentzlaff, D.[David], Kung, S.Y.,
Class-Discriminative CNN Compression,
ICPR22(2070-2077)
IEEE DOI 2212
Training, Measurement, Semantics, Redundancy, Neural networks, Fitting, Information filters BibRef

Fu, S.M.[Si-Ming], Wang, H.L.[Hua-Liang], Cao, Y.C.[Yu-Chen], Hu, H.J.[Hao-Ji], Peng, B.[Bo], Tan, W.M.[Wen-Ming], Ye, T.Q.[Ting-Qun],
Meta-BNS FOR Adversarial Data-Free Quantization,
ICIP22(4038-4042)
IEEE DOI 2211
Quantization (signal), Games, Generators, Data models, Convergence, Data-free Quantization, Meta-BNS, Adversarial Explore BibRef

Wang, W.[Wei], Chen, Z.[Zhuo], Wang, Z.[Zhe], Lin, J.[Jie], Xu, L.[Long], Lin, W.S.[Wei-Si],
Channel-Wise Bit Allocation for Deep Visual Feature Quantization,
ICIP22(3978-3982)
IEEE DOI 2211
Visualization, Quantization (signal), Image coding, Costs, Bit rate, Neural networks, Collaboration, Deep feature coding, edge-cloud collaboration BibRef

Tech, G.[Gerhard], Haase, P.[Paul], Becking, D.[Daniel], Kirchhoffer, H.[Heiner], Müller, K.[Karsten], Pfaff, J.[Jonathan], Schwarz, H.[Heiko], Samek, W.[Wojciech], Marpe, D.[Detlev], Wiegand, T.[Thomas],
History Dependent Significance Coding for Incremental Neural Network Compression,
ICIP22(3541-3545)
IEEE DOI 2211
Image coding, Federated learning, ISO Standards, Transform coding, Estimation, Artificial neural networks, machine learning BibRef

Santamaria, M.[Maria], Cricri, F.[Francesco], Lainema, J.[Jani], Youvalari, R.G.[Ramin G.], Zhang, H.L.[Hong-Lei], Hannuksela, M.M.[Miska M.],
Content-Adaptive Neural Network Post-Processing Filter with NNR-Coded Weight-Updates,
ICIP22(2251-2255)
IEEE DOI 2211
Video coding, Image coding, Bit rate, Artificial neural networks, Neural network compression, Filtering algorithms, Decoding, NNR, VVC BibRef

Chen, T.A.[Ting-An], Yang, D.N.[De-Nian], Chen, M.S.[Ming-Syan],
AlignQ: Alignment Quantization with ADMM-based Correlation Preservation,
CVPR22(12528-12537)
IEEE DOI 2210
Training, Quantization (signal), Correlation, Distributed databases, Benchmark testing, Minimization, Statistical methods BibRef

Chen, B.[Bo], Bakhshi, A.[Ali], Batista, G.[Gustavo], Ng, B.[Brian], Chin, T.J.[Tat-Jun],
Update Compression for Deep Neural Networks on the Edge,
MobileAI22(3075-3085)
IEEE DOI 2210
Deep learning, Neural networks, Refining, Redundancy, Bandwidth, Data models, Pattern recognition BibRef

Sun, X.L.[Xing-Long], Hassani, A.[Ali], Wang, Z.Y.[Zhang-Yang], Huang, G.[Gao], Shi, H.[Humphrey],
DiSparse: Disentangled Sparsification for Multitask Model Compression,
CVPR22(12372-12382)
IEEE DOI 2210
Training, Learning systems, Analytical models, Codes, Computational modeling, Machine learning BibRef

Dong, X.[Xin], de Salvo, B.[Barbara], Li, M.[Meng], Liu, C.[Chiao], Qu, Z.[Zhongnan], Kung, H.T., Li, Z.[Ziyun],
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems,
CVPR22(12549-12559)
IEEE DOI 2210
Performance evaluation, System performance, Neural networks, Sensor fusion, Cameras, Vision applications and systems BibRef

Hou, Z.J.[Ze-Jiang], Qin, M.H.[Ming-Hai], Sun, F.[Fei], Ma, X.L.[Xiao-Long], Yuan, K.[Kun], Xu, Y.[Yi], Chen, Y.K.[Yen-Kuang], Jin, R.[Rong], Xie, Y.[Yuan], Kung, S.Y.[Sun-Yuan],
CHEX: CHannel EXploration for CNN Model Compression,
CVPR22(12277-12288)
IEEE DOI 2210
Training, Costs, Image coding, Computational modeling, Pattern recognition, Efficient learning and inferences BibRef

Yin, M.[Miao], Sui, Y.[Yang], Yang, W.[Wanzhao], Zang, X.[Xiao], Gong, Y.[Yu], Yuan, B.[Bo],
HODEC: Towards Efficient High-Order DEcomposed Convolutional Neural Networks,
CVPR22(12289-12298)
IEEE DOI 2210
Image coding, Systematics, Convolution, Computational modeling, Computational efficiency, Pattern recognition, Efficient learning and inferences BibRef

Alwani, M.[Manoj], Wang, Y.[Yang], Madhavan, V.[Vashisht],
DECORE: Deep Compression with Reinforcement Learning,
CVPR22(12339-12349)
IEEE DOI 2210
Deep learning, Training, Visualization, Image coding, Memory management, Reinforcement learning, Network architecture, Optimization methods BibRef

Wang, H.Y.[Huan-Yu], Liu, J.J.[Jun-Jie], Ma, X.[Xin], Yong, Y.[Yang], Chai, Z.H.[Zhen-Hua], Wu, J.X.[Jian-Xin],
Compressing Models with Few Samples: Mimicking then Replacing,
CVPR22(691-700)
IEEE DOI 2210
Representation learning, Deep learning, Codes, Reconstruction algorithms, Transfer/low-shot/long-tail learning BibRef

Kag, A.[Anil], Saligrama, V.[Venkatesh],
Condensing CNNs with Partial Differential Equations,
CVPR22(600-609)
IEEE DOI 2210
Training, Partial differential equations, Computational modeling, Transforms, Mathematical models, Efficient learning and inferences BibRef

Chikin, V.[Vladimir], Antiukh, M.[Mikhail],
Data-Free Network Compression via Parametric Non-uniform Mixed Precision Quantization,
CVPR22(450-459)
IEEE DOI 2210
Deep learning, Training, Privacy, Quantization (signal), Computational modeling, Data models, Optimization methods, Deep learning architectures and techniques BibRef

Ren, Y.X.[Yu-Xi], Wu, J.[Jie], Xiao, X.F.[Xue-Feng], Yang, J.C.[Jian-Chao],
Online Multi-Granularity Distillation for GAN Compression,
ICCV21(6773-6783)
IEEE DOI 2203
Image quality, Visualization, Image coding, Redundancy, Generative adversarial networks, Generators, Image and video synthesis BibRef

Lohit, S.[Suhas], Jones, M.[Michael],
Model Compression Using Optimal Transport,
WACV22(3645-3654)
IEEE DOI 2202
Knowledge engineering, Training, Deep learning, Image coding, Computational modeling, Mobile handsets, Learning and Optimization BibRef

Tayyab, M.[Muhammad], Khan, F.A.[Fahad Ahmad], Mahalanobis, A.[Abhijit],
Compressing Deep CNNs Using Basis Representation and Spectral Fine-Tuning,
ICIP21(3537-3541)
IEEE DOI 2201
Image coding, Convolution, Object detection, Spatial filters, Convolutional neural networks, Image classification, orthogonal filters BibRef

Papadimitriou, D.[Dimitris], Jain, S.[Swayambhoo],
Data-Driven Low-Rank Neural Network Compression,
ICIP21(3547-3551)
IEEE DOI 2201
Deep learning, Image coding, Neural networks, Convex functions, Artificial intelligence, Deep Neural Network Compression, Edge AI BibRef

Afrabandpey, H.[Homayun], Muravev, A.[Anton], Tavakoli, H.R.[Hamed R.], Zhang, H.L.[Hong-Lei], Cricri, F.[Francesco], Gabbouj, M.[Moncef], Aksu, E.[Emre],
Mind the Structure: Adopting Structural Information for Deep Neural Network Compression,
ICIP21(3532-3536)
IEEE DOI 2201
Deep learning, Quantization (signal), Image coding, Image analysis, Neural networks, Focusing, Acoustics, Clustering BibRef

Idelbayev, Y.[Yerlan], Carreira-Perpiñán, M.Á.[Miguel Á.],
Beyond Flops In Low-Rank Compression of Neural Networks: Optimizing Device-Specific Inference Runtime,
ICIP21(2843-2847)
IEEE DOI 2201
Performance evaluation, Image coding, Runtime, Neural networks, Time measurement, Inference algorithms, neural network compression BibRef

Choi, Y.[Yoojin], El-Khamy, M.[Mostafa], Lee, J.[Jungwon],
Zero-Shot Learning of A Conditional Generative Adversarial Network for Data-Free Network Quantization,
ICIP21(3552-3556)
IEEE DOI 2201
Training, Quantization (signal), Image coding, Pipelines, Neural networks, Training data, Generative adversarial networks, quantization BibRef

Kondratyuk, D.[Dan], Yuan, L.Z.[Liang-Zhe], Li, Y.D.[Yan-Dong], Zhang, L.[Li], Tan, M.X.[Ming-Xing], Brown, M.[Matthew], Gong, B.Q.[Bo-Qing],
MoViNets: Mobile Video Networks for Efficient Video Recognition,
CVPR21(16015-16025)
IEEE DOI 2111
Training, Costs, Computational modeling, Memory management, Video sequences, Computational efficiency BibRef

Yu, C.Q.[Chang-Qian], Xiao, B.[Bin], Gao, C.X.[Chang-Xin], Yuan, L.[Lu], Zhang, L.[Lei], Sang, N.[Nong], Wang, J.D.[Jing-Dong],
Lite-HRNet: A Lightweight High-Resolution Network,
CVPR21(10435-10445)
IEEE DOI 2111
Convolutional codes, Bridges, Computational modeling, Pose estimation, Semantics, Pattern recognition BibRef

Li, Y.C.[Yu-Chao], Lin, S.H.[Shao-Hui], Liu, J.Z.[Jian-Zhuang], Ye, Q.X.[Qi-Xiang], Wang, M.[Mengdi], Chao, F.[Fei], Yang, F.[Fan], Ma, J.C.[Jin-Cheng], Tian, Q.[Qi], Ji, R.R.[Rong-Rong],
Towards Compact CNNs via Collaborative Compression,
CVPR21(6434-6443)
IEEE DOI 2111
Image coding, Tensors, Sensitivity, Collaboration, Transforms, Performance gain, Pattern recognition BibRef

Shen, Z.Q.[Zhi-Qiang], Liu, Z.[Zechun], Qin, J.[Jie], Huang, L.[Lei], Cheng, K.T.[Kwang-Ting], Savvides, M.[Marios],
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration,
CVPR21(2165-2174)
IEEE DOI 2111

WWW Link. Code, Learning. Training, Degradation, Codes, Neural networks, Supervised learning, Predictive models BibRef

Yin, M.[Miao], Sui, Y.[Yang], Liao, S.[Siyu], Yuan, B.[Bo],
Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework,
CVPR21(10669-10678)
IEEE DOI 2111
Tensors, Image coding, Systematics, Recurrent neural networks, Image recognition, Computational modeling, Convex functions BibRef

Martinez, J.[Julieta], Shewakramani, J.[Jashan], Liu, T.W.[Ting Wei], Bârsan, I.A.[Ioan Andrei], Zeng, W.Y.[Wen-Yuan], Urtasun, R.[Raquel],
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks,
CVPR21(15694-15703)
IEEE DOI 2111
Convolutional codes, Visualization, Image coding, Annealing, Vector quantization, Neural networks, Rate-distortion BibRef

Oh, S.[Sangyun], Sim, H.[Hyeonuk], Lee, S.[Sugil], Lee, J.[Jongeun],
Automated Log-Scale Quantization for Low-Cost Deep Neural Networks,
CVPR21(742-751)
IEEE DOI 2111
Training, Deep learning, Image segmentation, Quantization (signal), Semantics, Computer architecture BibRef

Yamamoto, K.[Kohei],
Learnable Companding Quantization for Accurate Low-bit Neural Networks,
CVPR21(5027-5036)
IEEE DOI 2111
Training, Quantization (signal), Limiting, Memory management, Neural networks, Object detection, Table lookup BibRef

Lee, J.[Junghyup], Kim, D.[Dohyung], Ham, B.[Bumsub],
Network Quantization with Element-wise Gradient Scaling,
CVPR21(6444-6453)
IEEE DOI 2111
Training, Deep learning, Quantization (signal), Network architecture, Hardware BibRef

Jaume, G.[Guillaume], Pati, P.[Pushpak], Bozorgtabar, B.[Behzad], Foncubierta, A.[Antonio], Anniciello, A.M.[Anna Maria], Feroce, F.[Florinda], Rau, T.[Tilman], Thiran, J.P.[Jean-Philippe], Gabrani, M.[Maria], Goksel, O.[Orcun],
Quantifying Explainers of Graph Neural Networks in Computational Pathology,
CVPR21(8102-8112)
IEEE DOI 2111
Measurement, Deep learning, Pathology, Terminology, Satellite broadcasting, Radiology, Breast cancer BibRef

Zhao, S.[Sijie], Yue, T.[Tao], Hu, X.M.[Xue-Mei],
Distribution-aware Adaptive Multi-bit Quantization,
CVPR21(9277-9286)
IEEE DOI 2111
Training, Quantization (signal), Sensitivity, Neural networks, Taylor series, Pattern recognition, Resource management BibRef

Kryzhanovskiy, V.[Vladimir], Balitskiy, G.[Gleb], Kozyrskiy, N.[Nikolay], Zuruev, A.[Aleksandr],
QPP: Real-Time Quantization Parameter Prediction for Deep Neural Networks,
CVPR21(10679-10687)
IEEE DOI 2111
Deep learning, Training, Quantization (signal), Runtime, Superresolution, Predictive models, Stability analysis BibRef

Aghli, N.[Nima], Ribeiro, E.[Eraldo],
Combining Weight Pruning and Knowledge Distillation For CNN Compression,
EVW21(3185-3192)
IEEE DOI 2109
Image coding, Neurons, Estimation, Graphics processing units, Real-time systems, Convolutional neural networks BibRef

Ran, J.[Jie], Lin, R.[Rui], So, H.K.H.[Hayden K.H.], Chesi, G.[Graziano], Wong, N.[Ngai],
Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks,
ICPR21(9866-9873)
IEEE DOI 2105
Training, Tensors, Neural networks, Redundancy, Games, Elasticity, Minimization BibRef

Shah, M.A.[Muhammad A.], Olivier, R.[Raphael], Raj, B.[Bhiksha],
Exploiting Non-Linear Redundancy for Neural Model Compression,
ICPR21(9928-9935)
IEEE DOI 2105
Training, Image coding, Computational modeling, Neurons, Transfer learning, Redundancy, Nonlinear filters BibRef

Bui, K.[Kevin], Park, F.[Fredrick], Zhang, S.[Shuai], Qi, Y.Y.[Ying-Yong], Xin, J.[Jack],
Nonconvex Regularization for Network Slimming: Compressing CNNS Even More,
ISVC20(I:39-53).
Springer DOI 2103
BibRef

Wang, H.T.[Hao-Tao], Gui, S.P.[Shu-Peng], Yang, H.C.[Hai-Chuan], Liu, J.[Ji], Wang, Z.Y.[Zhang-Yang],
GAN Slimming: All-in-one GAN Compression by a Unified Optimization Framework,
ECCV20(IV:54-73).
Springer DOI 2011
BibRef

Guo, J., Ouyang, W., Xu, D.,
Multi-Dimensional Pruning: A Unified Framework for Model Compression,
CVPR20(1505-1514)
IEEE DOI 2008
Tensile stress, Redundancy, Logic gates, Convolution, Solid modeling 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

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

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


Last update:May 23, 2024 at 14:31:23