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2011
Deep learning, Network pruning,
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Deep neural network (DNN), Convolutional neural network (CNN),
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2106
Convolutional neural networks (CNNs), Depth redundancy,
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Learning Adjustable Reduced Downsampling Network for Small Object
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RS(13), No. 18, 2021, pp. xx-yy.
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2112
Adaptive dilated convolution, Representation learning, Image classification
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Optimization-Based Neural Networks Compression,
ICIP21(3512-3516)
IEEE DOI
2201
Performance evaluation, Image coding, Neurons, Memory management,
Task analysis, Biological neural networks,
Distillation
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Contraction of Dynamically Masked Deep Neural Networks for Efficient
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IEEE DOI
2202
Neurons, Taylor series, Surveillance, Sparse matrices,
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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
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Kirchhoffer, H.[Heiner],
Haase, P.[Paul],
Samek, W.[Wojciech],
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Aksu, E.B.[Emre B.],
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Jiang, W.[Wei],
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Overview of the Neural Network Compression and Representation (NNR)
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CirSysVideo(32), No. 5, May 2022, pp. 3203-3216.
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2205
Artificial neural networks, Quantization (signal),
Biological neural networks, Standards, Tensors, Decoding, Training,
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Ji, Y.W.[Yu-Wang],
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Fast CP-compression layer:
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2206
tensor Canonical Polyadic.
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Self-Distillation: Towards Efficient and Compact Neural Networks,
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2207
Neural networks, Knowledge engineering, Training,
Computational modeling, Acceleration,
image classification
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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
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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
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Automatic Deployment of Convolutional Neural Networks on FPGA for
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RS(14), No. 13, 2022, pp. xx-yy.
DOI Link
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Liu, Y.F.[Yu-Fan],
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PAMI(45), No. 3, March 2023, pp. 3378-3395.
IEEE DOI
2302
Training, Optimization, Knowledge engineering, Computational modeling,
Analytical models, Heuristic algorithms, deep learning
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Tian, Q.[Qing],
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CVIU(231), 2023, pp. 103682.
Elsevier DOI
2305
Deep neural network pruning, Deep discriminant analysis,
Deep representation learning
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Guo, S.[Suhan],
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PR(140), 2023, pp. 109508.
Elsevier DOI
2305
Filter pruning, Saliency-based pruning,
End-to-end pruning framework, Sampling bias
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Zhu, Y.Y.[Yang-Yang],
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FSConv: Flexible and separable convolution for convolutional neural
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PR(140), 2023, pp. 109589.
Elsevier DOI
2305
CNNs compression, Representative feature maps,
Redundant feature maps, Intrinsic information, Tiny hidden details
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Lu, W.Z.[Wei-Zhi],
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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
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Guo, L.[Lie],
Zhao, Y.B.[Yi-Bing],
Gao, J.D.[Jian-Dong],
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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
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Zhang, C.Y.[Chao-Yan],
Li, C.[Cheng],
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Neural Network Compression via Low Frequency Preference,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link
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Efficient Layer Compression Without Pruning,
IP(32), 2023, pp. 4689-4700.
IEEE DOI
2309
BibRef
Chen, J.[Jun],
Bai, S.P.[Shi-Peng],
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Data-Free Quantization via Mixed-Precision Compensation without
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PR(143), 2023, pp. 109780.
Elsevier DOI
2310
Neural network compression, Date-free quantization
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Bai, S.P.[Shi-Peng],
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Unified Data-Free Compression: Pruning and Quantization without
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ICCV23(5853-5862)
IEEE DOI
2401
BibRef
Duan, W.H.[Wen-Hong],
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Differential Weight Quantization for Multi-Model Compression,
MultMed(25), 2023, pp. 6397-6410.
IEEE DOI
2311
quantization in deep network
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Lan, W.C.[Wei-Chao],
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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],
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Multi-Dimension Compression of Feed-Forward Network in Vision
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PRL(176), 2023, pp. 56-61.
Elsevier DOI
2312
Vision Transformers, Feed-Forward Network, Pruning, FLOPs, Parameters
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Wang, Z.Y.[Zhen-Yu],
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Zhao, Q.[Qinghang],
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Filter Clustering for Compressing CNN Model With Better Feature
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CirSysVideo(33), No. 12, December 2023, pp. 7385-7397.
IEEE DOI
2312
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Tan, Q.F.[Qi-Fan],
Yang, X.[Xuqi],
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SCCMDet: Adaptive Sparse Convolutional Networks Based on Class Maps
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Elsevier DOI
2405
Lightweight neural network, Vision transformer,
Real-time semantic segmentation, Multi-scale fusion, Attention mechanism
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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],
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ICCV23(17500-17510)
IEEE DOI Code:
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2401
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PRANC: Pseudo RAndom Networks for Compacting deep models,
ICCV23(16975-16985)
IEEE DOI Code:
WWW Link.
2401
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Lee, A.H.X.[Alina Hui Xiu],
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Lin, W.S.[Wei-Si],
Metagrad: Adaptive Gradient Quantization with Hypernetworks,
ICIP23(276-280)
IEEE DOI
2312
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Schaub-Meyer, S.[Simone],
Roth, S.[Stefan],
Content-Adaptive Downsampling in Convolutional Neural Networks,
ECV23(4544-4553)
IEEE DOI
2309
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Hu, T.[Tie],
Lin, M.[Mingbao],
You, L.[Lizhou],
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Discriminator-Cooperated Feature Map Distillation for GAN Compression,
CVPR23(20351-20360)
IEEE DOI
2309
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Yakovleva, A.[Alexandra],
Buchnev, V.[Valentin],
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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],
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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
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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],
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Pbcstereo: A Compressed Stereo Network with Pure Binary Convolutional
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ACCV22(III:626-641).
Springer DOI
2307
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Kim, S.[Soyeong],
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Moon, J.[Jaekyun],
Deep Neural Network Compression for Image Inpainting,
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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.Y.[Zi-Yun],
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
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CVPR21(6434-6443)
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2111
Image coding, Tensors, Sensitivity, Collaboration, Transforms,
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CVPR21(10669-10678)
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feature extraction, image classification, image segmentation,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Net Quantization .