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Convolutional neural network, CNN compression,
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2011
Deep learning, Network pruning,
Network compressing, Top-down attention, Perceptual visioning
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Deep neural network (DNN), Convolutional neural network (CNN),
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2106
Convolutional neural networks (CNNs), Depth redundancy,
Entropy, Feature compression, EBCLE
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Zhang, H.J.[Hui-Jie],
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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
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Yao, J.[Jie],
Wang, D.D.[Dong-Dong],
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ADCNN: Towards learning adaptive dilation for convolutional neural
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PR(123), 2022, pp. 108369.
Elsevier DOI
2112
Adaptive dilated convolution, Representation learning, Image classification
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Tahiri, Y.[Younes],
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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],
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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)
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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.
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Zhang, L.F.[Lin-Feng],
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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
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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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Lai, S.Q.[Shen-Qi],
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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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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
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RS(14), No. 13, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Liu, Y.F.[Yu-Fan],
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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
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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
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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
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Sun, X.L.[Xing-Long],
Hassani, A.[Ali],
Wang, Z.Y.[Zhang-Yang],
Huang, G.[Gao],
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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
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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
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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
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Lee, J.[Junghyup],
Kim, D.[Dohyung],
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Network Quantization with Element-wise Gradient Scaling,
CVPR21(6444-6453)
IEEE DOI
2111
Training, Deep learning, Quantization (signal),
Network architecture, Hardware
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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
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Yue, T.[Tao],
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Distribution-aware Adaptive Multi-bit Quantization,
CVPR21(9277-9286)
IEEE DOI
2111
Training, Quantization (signal), Sensitivity, Neural networks,
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Kryzhanovskiy, V.[Vladimir],
Balitskiy, G.[Gleb],
Kozyrskiy, N.[Nikolay],
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QPP: Real-Time Quantization Parameter Prediction for Deep Neural
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CVPR21(10679-10687)
IEEE DOI
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Deep learning, Training, Quantization (signal), Runtime,
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Aghli, N.[Nima],
Ribeiro, E.[Eraldo],
Combining Weight Pruning and Knowledge Distillation For CNN
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EVW21(3185-3192)
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Image coding, Neurons, Estimation, Graphics processing units,
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Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks,
ICPR21(9866-9873)
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Training, Tensors, Neural networks, Redundancy, Games, Elasticity, Minimization
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Shah, M.A.[Muhammad A.],
Olivier, R.[Raphael],
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Exploiting Non-Linear Redundancy for Neural Model Compression,
ICPR21(9928-9935)
IEEE DOI
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Training, Image coding, Computational modeling, Neurons,
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Multi-Dimensional Pruning: A Unified Framework for Model Compression,
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Tensile stress, Redundancy,
Logic gates, Convolution, Solid modeling
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Heo, B.[Byeongho],
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ICCV19(1921-1930)
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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 .