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.J.[Stephen J.],
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.R.[Ming-Rui],
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.K.[Zhi-Kai],
Li, M.K.[Meng-Ke],
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
Hu, K.D.[Kai-Di],
Xie, Z.X.[Zong-Xia],
Hu, Q.H.[Qing-Hua],
Lightweight convolutional neural networks with context broadcast
transformer for real-time semantic segmentation,
IVC(146), 2024, pp. 105053.
Elsevier DOI
2405
Lightweight neural network, Vision transformer,
Real-time semantic segmentation, Multi-scale fusion, Attention mechanism
BibRef
Nguyen, T.T.[Thanh Tuan],
Nguyen, T.P.[Thanh Phuong],
Rescaling large datasets based on validation outcomes of a
pre-trained network,
PRL(185), 2024, pp. 73-80.
Elsevier DOI Code:
WWW Link.
2410
Statistical computation, Deep neural networks,
Rescaling large datasets, ImageNet, Places365
BibRef
Wang, H.X.[Hao-Xuan],
Ling, P.Y.[Peng-Yang],
Fan, X.[Xin],
Tu, T.[Tao],
Zheng, J.J.[Jin-Jin],
Chen, H.[Huaian],
Jin, Y.[Yi],
Chen, E.[Enhong],
All-in-One Hardware-Oriented Model Compression for Efficient
Multi-Hardware Deployment,
CirSysVideo(34), No. 12, December 2024, pp. 12345-12359.
IEEE DOI Code:
WWW Link.
2501
Hardware, Costs, Data models, Filters, Graphics processing units,
Training, Accuracy, Convolutional neural network (CNN),
multi-hardware deployment scenarios
BibRef
Yang, S.[Sai],
Liu, F.[Fan],
Chen, D.[Delong],
Huang, H.X.[Hua-Xi],
Zhou, J.[Jun],
Few-Shot Classification Model Compression via School Learning,
CirSysVideo(34), No. 12, December 2024, pp. 12244-12257.
IEEE DOI
2501
Power capacitors, Knowledge engineering, Task analysis,
Metalearning, Accuracy, Feature extraction, Training, knowledge distillation
BibRef
Gao, C.S.[Chang-Sheng],
Jiang, Y.H.[Yi-Heng],
Wu, S.Q.[Si-Qi],
Ma, Y.F.[Yi-Fan],
Li, L.[Li],
Liu, D.[Dong],
IMOFC: Identity-Level Metric Optimized Feature Compression for
Identification Tasks,
CirSysVideo(35), No. 2, February 2025, pp. 1855-1869.
IEEE DOI
2502
Image coding, Distortion, Feature extraction,
Distortion measurement, Semantics, Image reconstruction,
inter-identity discriminability
BibRef
Justo, J.A.[Jon Alvarez],
Langer, D.D.[Dennis D.],
Berg, S.[Simen],
Nieke, J.[Jens],
Ionescu, R.T.[Radu Tudor],
Kjeldsberg, P.G.[Per Gunnar],
Johansen, T.A.[Tor Arne],
Hyperspectral Image Segmentation for Optimal Satellite Operations:
In-Orbit Deployment of 1D-CNN,
RS(17), No. 4, 2025, pp. 642.
DOI Link
2502
BibRef
Zhang, L.L.[Long-Long],
Hu, X.[Xiang],
Liao, X.[Xuan],
Zhou, T.[Tong],
Peng, Y.Y.X.[Yuan-Yan-Xi],
FLQ: Design and implementation of hybrid multi-base full logarithmic
quantization neural network acceleration architecture based on FPGA,
SP:IC(134), 2025, pp. 117270.
Elsevier DOI
2503
Deep neural network, Logarithmic quantization, Computing units, FPGAs
BibRef
Fan, C.X.[Chun-Xiao],
Guo, D.[Dan],
Wang, Z.Q.[Zi-Qi],
Wang, M.[Meng],
Multi-Objective Convex Quantization for Efficient Model Compression,
PAMI(47), No. 4, April 2025, pp. 2313-2329.
IEEE DOI
2503
BibRef
Earlier: A1, A3, A2, A4:
Data-Free Quantization via Pseudo-label Filtering,
CVPR24(5589-5598)
IEEE DOI
2410
Quantization (signal), Training, Optimization, Accuracy, Convolution,
Computational modeling, Training data, Costs, Calibration,
multi-objective optimization.
Training, Measurement, Quantization (signal), Filtering,
Training data, Reliability engineering
BibRef
Fischer, K.[Kristian],
Brand, F.[Fabian],
Kaup, A.[André],
Boosting Neural Image Compression for Machines Using Latent Space
Masking,
CirSysVideo(35), No. 4, April 2025, pp. 3719-3731.
IEEE DOI
2504
Encoding, Image coding, Task analysis, Training, Neural networks,
Machine-to-machine communications, Bit rate
BibRef
Cai, Y.G.[Yan-Gang],
Xing, P.[Peiyin],
Gao, X.S.[Xue-Song],
High Efficient 3D Convolution Feature Compression,
CirSysVideo(35), No. 4, April 2025, pp. 3732-3744.
IEEE DOI
2504
Feature quantization.
Feature extraction, Quantization (signal),
Convolutional neural networks, Image coding, Servers,
action recognition
BibRef
Yang, X.X.[Xiao-Xia],
Zheng, Z.S.[Zhi-Shuai],
Zheng, H.Q.[Huan-Qi],
Ge, Z.D.[Zhe-Dong],
Liu, X.T.[Xiao-Tong],
Zhang, B.[Bei],
Lv, J.Y.[Jin-Yang],
LCNet: Lightweight real-time image classification network based on
efficient multipath dynamic attention mechanism and dynamic threshold
convolution,
IVC(159), 2025, pp. 105576.
Elsevier DOI
2505
Visual classification, Multipath dynamic attention mechanism,
Dynamic threshold convolution, Star operation, Multipath architecture
BibRef
Yang, X.Y.[Xing-Yi],
Wang, X.C.[Xin-Chao],
Neural Metamorphosis,
ECCV24(XXXIV: 1-19).
Springer DOI
2412
BibRef
Xiong, Y.Z.[Yi-Zhe],
Chen, H.[Hui],
Hao, T.X.[Tian-Xiang],
Lin, Z.[Zijia],
Han, J.G.[Jun-Gong],
Zhang, Y.[Yuesong],
Wang, G.X.[Guo-Xin],
Bao, Y.J.[Yong-Jun],
Ding, G.[Guiguang],
PYRA: Parallel Yielding Re-activation for Training-inference Efficient
Task Adaptation,
ECCV24(IX: 455-473).
Springer DOI
2412
Code:
WWW Link.
BibRef
Misra, D.[Diganta],
Chaudhary, M.[Muawiz],
Goyal, A.[Agam],
Runwal, B.[Bharat],
Chen, P.Y.[Pin Yu],
Uncovering the Hidden Cost of Model Compression,
Prompting24(1611-1621)
IEEE DOI
2410
Visualization, Adaptation models, Solid modeling, Costs,
Quantization (signal), Computational modeling, Sparsity, Quantization
BibRef
Xie, J.J.[Jing-Jing],
Zhang, Y.X.[Yu-Xin],
Lin, M.[Mingbao],
Lin, Z.H.[Zhi-Hang],
Cao, L.J.[Liu-Juan],
Ji, R.R.[Rong-Rong],
UniPTS: A Unified Framework for Proficient Post-Training Sparsity,
CVPR24(5746-5755)
IEEE DOI Code:
WWW Link.
2410
Training, Knowledge engineering, Degradation, Codes,
Heuristic algorithms, Employment, Post-training sparsity, model compression
BibRef
Zhang, H.X.[Han-Xiao],
Zhou, Y.F.[Yi-Fan],
Wang, G.H.[Guo-Hua],
Dense Vision Transformer Compression with Few Samples,
CVPR24(15825-15834)
IEEE DOI
2410
Training, Accuracy, Computational modeling, Transformers,
Complexity theory, Vision Transformer, Pruning
BibRef
Nasery, A.[Anshul],
Shah, H.[Hardik],
Suggala, A.S.[Arun Sai],
Jain, P.[Prateek],
End-to-End Neural Network Compression via l1/l2 Regularized Latency
Surrogates,
MobileAI24(5866-5877)
IEEE DOI
2410
Training, Quantization (signal), Image coding, Accuracy, Costs,
Transfer learning, Computer architecture
BibRef
Guo, Y.[Yipin],
Li, Z.[Zihao],
Lang, Y.L.[Yi-Lin],
Ren, Q.Y.[Qin-Yuan],
ShiftAddAug: Augment Multiplication-Free Tiny Neural Network with
Hybrid Computation,
ECV24(8075-8084)
IEEE DOI
2410
Training, Accuracy, Computational modeling, Semantic segmentation,
Hardware, tiny neural network, multiplication-free network
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
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
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,
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, 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
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
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
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
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, 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 .