Dong, Y.P.[Yin-Peng],
Ni, R.K.[Ren-Kun],
Li, J.G.[Jian-Guo],
Chen, Y.R.[Yu-Rong],
Su, H.[Hang],
Zhu, J.[Jun],
Stochastic Quantization for Learning Accurate Low-Bit Deep Neural
Networks,
IJCV(127), No. 11-12, December 2019, pp. 1629-1642.
Springer DOI
1911
BibRef
Zhou, A.[Aojun],
Yao, A.B.[An-Bang],
Wang, K.[Kuan],
Chen, Y.R.[Yu-Rong],
Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural
Networks,
CVPR18(9426-9435)
IEEE DOI
1812
Pattern recognition
BibRef
Zhou, Z.G.[Zheng-Guang],
Zhou, W.G.[Wen-Gang],
Lv, X.T.[Xu-Tao],
Huang, X.[Xuan],
Wang, X.Y.[Xiao-Yu],
Li, H.Q.[Hou-Qiang],
Progressive Learning of Low-Precision Networks for Image
Classification,
MultMed(23), 2021, pp. 871-882.
IEEE DOI
2103
Quantization (signal), Training, Neural networks, Convolution,
Acceleration, Task analysis, Complexity theory,
image classification
BibRef
Chu, T.S.[Tian-Shu],
Luo, Q.[Qin],
Yang, J.[Jie],
Huang, X.L.[Xiao-Lin],
Mixed-precision quantized neural networks with progressively
decreasing bitwidth,
PR(111), 2021, pp. 107647.
Elsevier DOI
2012
Model compression, Quantized neural networks, Mixed-precision
BibRef
Zhuang, B.[Bohan],
Tan, M.K.[Ming-Kui],
Liu, J.[Jing],
Liu, L.Q.[Ling-Qiao],
Reid, I.D.[Ian D.],
Shen, C.H.[Chun-Hua],
Effective Training of Convolutional Neural Networks With Low-Bitwidth
Weights and Activations,
PAMI(44), No. 10, October 2022, pp. 6140-6152.
IEEE DOI
2209
BibRef
Earlier: A1, A6, A2, A3, A5, Only:
Towards Effective Low-Bitwidth Convolutional Neural Networks,
CVPR18(7920-7928)
IEEE DOI
1812
Training, Quantization (signal), Neural networks,
Stochastic processes, Numerical models, Knowledge engineering,
image classification.
Optimization, Hardware, Convolution
BibRef
Liu, J.[Jing],
Zhuang, B.[Bohan],
Chen, P.[Peng],
Shen, C.H.[Chun-Hua],
Cai, J.F.[Jian-Fei],
Tan, M.K.[Ming-Kui],
Single-Path Bit Sharing for Automatic Loss-Aware Model Compression,
PAMI(45), No. 10, October 2023, pp. 12459-12473.
IEEE DOI
2310
BibRef
Wu, R.[Ran],
Liu, H.Y.[Huan-Yu],
Li, J.B.[Jun-Bao],
Adaptive gradients and weight projection based on quantized neural
networks for efficient image classification,
CVIU(223), 2022, pp. 103516.
Elsevier DOI
2210
Quantization, Deep projection, Adaptive gradients,
High dimensional training space
BibRef
Wang, P.S.[Pei-Song],
Chen, W.H.[Wei-Han],
He, X.Y.[Xiang-Yu],
Chen, Q.[Qiang],
Liu, Q.S.[Qing-Shan],
Cheng, J.[Jian],
Optimization-Based Post-Training Quantization With Bit-Split and
Stitching,
PAMI(45), No. 2, February 2023, pp. 2119-2135.
IEEE DOI
2301
Quantization (signal), Training, Tensors, Optimization,
Network architecture, Degradation, Task analysis,
post-training quantization
BibRef
Li, Z.[Zefan],
Ni, B.B.[Bing-Bing],
Yang, X.K.[Xiao-Kang],
Zhang, W.J.[Wen-Jun],
Gao, W.[Wen],
Residual Quantization for Low Bit-Width Neural Networks,
MultMed(25), 2023, pp. 214-227.
IEEE DOI
2301
Quantization (signal), Training, Computational modeling, Neurons,
Degradation, Task analysis, Optimization, Deep learning,
network acceleration
BibRef
Sharma, P.K.[Prasen Kumar],
Abraham, A.[Arun],
Rajendiran, V.N.[Vikram Nelvoy],
A Generalized Zero-Shot Quantization of Deep Convolutional Neural
Networks Via Learned Weights Statistics,
MultMed(25), 2023, pp. 953-965.
IEEE DOI
2303
Quantization (signal), Training, Data models, Tensors, Calibration,
Computational modeling, Convolutional neural networks,
post-training quantization
BibRef
Xu, S.K.[Shou-Kai],
Zhang, S.H.[Shu-Hai],
Liu, J.[Jing],
Zhuang, B.[Bohan],
Wang, Y.W.[Yao-Wei],
Tan, M.K.[Ming-Kui],
Generative Data Free Model Quantization With Knowledge Matching for
Classification,
CirSysVideo(33), No. 12, December 2023, pp. 7296-7309.
IEEE DOI Code:
WWW Link.
2312
BibRef
Kazemi, E.[Ehsan],
Taherkhani, F.[Fariborz],
Wang, L.Q.[Li-Qiang],
On complementing unsupervised learning with uncertainty
quantification,
PRL(176), 2023, pp. 69-75.
Elsevier DOI
2312
Uncertainty quantification, Semi-supervised learning,
Approximate Bayesian models, Confirmation bias
BibRef
Sun, S.Z.[Shu-Zhou],
Xu, H.[Huali],
Li, Y.[Yan],
Li, P.[Ping],
Sheng, B.[Bin],
Lin, X.[Xiao],
FastAL: Fast Evaluation Module for Efficient Dynamic Deep Active
Learning Using Broad Learning System,
CirSysVideo(34), No. 2, February 2024, pp. 815-827.
IEEE DOI
2402
Data models, Training, Uncertainty, Learning systems,
Frequency-domain analysis, Costs, Predictive models,
deep learning
BibRef
Chu, T.S.[Tian-Shu],
Yang, Z.[Zuopeng],
Huang, X.L.[Xiao-Lin],
Improving the Post-Training Neural Network Quantization by
Prepositive Feature Quantization,
CirSysVideo(34), No. 4, April 2024, pp. 3056-3060.
IEEE DOI
2404
Quantization (signal), Calibration, Optimization, Training,
Perturbation methods, Numerical models, Computational modeling,
post-training quantization
BibRef
Yvinec, E.[Edouard],
Dapogny, A.[Arnaud],
Bailly, K.[Kevin],
PIPE: Parallelized inference through ensembling of residual
quantization expansions,
PR(154), 2024, pp. 110571.
Elsevier DOI
2406
Quantization, Deep learning, LLM, Ensemble, Efficient inference
BibRef
Li, Z.K.[Zhi-Kai],
Long, X.L.[Xian-Lei],
Xiao, J.R.[Jun-Rui],
Gu, Q.Y.[Qing-Yi],
HTQ: Exploring the High-Dimensional Trade-Off of mixed-precision
quantization,
PR(156), 2024, pp. 110788.
Elsevier DOI Code:
WWW Link.
2408
Model compression, Quantized neural networks, Mixed-precision
BibRef
Zhong, Y.S.[Yun-Shan],
Zhou, Y.Y.[Yu-Yao],
Chao, F.[Fei],
Ji, R.R.[Rong-Rong],
MBQuant: A novel multi-branch topology method for arbitrary bit-width
network quantization,
PR(158), 2025, pp. 111061.
Elsevier DOI Code:
WWW Link.
2411
Network quantization, Quantization-aware training,
Arbitrary bit-width, Multi-branch topology
BibRef
Gongyo, S.[Shinya],
Liang, J.R.[Jin-Rong],
Ambai, M.[Mitsuru],
Kawakami, R.[Rei],
Sato, I.[Ikuro],
Learning Non-uniform Step Sizes for Neural Network Quantization,
ACCV24(VIII: 55-73).
Springer DOI
2412
BibRef
Makhov, D.[Denis],
Ostapets, R.[Ruslan],
Zhelavskaya, I.[Irina],
Song, D.H.[De-Hua],
Solodskikh, K.[Kirill],
Towards Robust Full Low-bit Quantization of Super Resolution Networks,
ECCV24(LXXV: 182-198).
Springer DOI
2412
BibRef
Pham, C.[Cuong],
Hoang, A.D.[Anh Dung],
Nguyen, C.C.[Cuong C.],
Le, T.[Trung],
Phung, D.[Dinh],
Carneiro, G.[Gustavo],
Do, T.T.[Thanh-Toan],
Metaaug: Meta-Data Augmentation for Post-training Quantization,
ECCV24(XXVII: 236-252).
Springer DOI
2412
BibRef
Hu, Z.C.[Zi-Cong],
Cao, J.[Jian],
Xu, W.C.[Wei-Chen],
Ren, R.L.[Rui-Long],
Fu, T.H.[Tian-Hao],
Xu, X.X.[Xin-Xin],
Zhang, X.[Xing],
Empirical Research on Quantization for 3D Multi-Modal ViT Models,
ICIP24(3606-3612)
IEEE DOI
2411
Solid modeling, Image segmentation, Quantization (signal),
Object detection, Transformers, ViT, Model Quantization,
BEV map segmentation
BibRef
Yang, L.W.[Lian-Wei],
Li, Z.K.[Zhi-Kai],
Xiao, J.[Junrui],
Gong, H.S.[Hai-Song],
Gu, Q.Y.[Qing-Yi],
MGRQ: Post-Training Quantization for Vision Transformer with Mixed
Granularity Reconstruction,
ICIP24(2744-2750)
IEEE DOI
2411
Degradation, Quantization (signal), Accuracy, Buildings,
Reconstruction algorithms, Transformers,
Reconstruction Optimization
BibRef
Kumar, A.[Ashish],
Kim, D.[Daneul],
Park, J.[Jaesik],
Behera, L.[Laxmidhar],
Pick-or-Mix: Dynamic Channel Sampling for ConvNets,
CVPR24(5873-5882)
IEEE DOI Code:
WWW Link.
2410
Convolutional codes, Accuracy, Computational modeling,
Computer architecture, Dynamic Channel Pruning
BibRef
Lu, X.Y.[Xiang-Yong],
Suganuma, M.[Masanori],
Okatani, T.[Takayuki],
SBCFormer: Lightweight Network Capable of Full-size ImageNet
Classification at 1 FPS on Single Board Computers,
WACV24(1112-1122)
IEEE DOI Code:
WWW Link.
2404
Smart agriculture, Visualization, Codes, Computational modeling,
Streaming media, Transformers, Algorithms, Image recognition and understanding
BibRef
Schaefer, C.J.S.[Clemens J.S.],
Joshi, S.[Siddharth],
Li, S.[Shan],
Blazquez, R.[Raul],
Edge Inference with Fully Differentiable Quantized Mixed Precision
Neural Networks,
WACV24(8445-8454)
IEEE DOI
2404
Training, Performance evaluation, Adaptation models, Schedules,
Quantization (signal), Costs, Image edge detection, Applications,
Embedded sensing / real-time techniques
BibRef
Sun, X.[Ximeng],
Panda, R.[Rameswar],
Chen, C.F.R.[Chun-Fu Richard],
Wang, N.[Naigang],
Pan, B.[Bowen],
Oliva, A.[Aude],
Feris, R.[Rogerio],
Saenko, K.[Kate],
Improved Techniques for Quantizing Deep Networks with Adaptive
Bit-Widths,
WACV24(946-956)
IEEE DOI
2404
Knowledge engineering, Adaptation models, Adaptive systems,
Quantization (signal), Collaboration, Artificial neural networks,
Image recognition and understanding
BibRef
Bamba, U.[Udbhav],
Anand, N.[Neeraj],
Aggarwal, S.[Saksham],
Prasad, D.K.[Dilip K.],
Gupta, D.K.[Deepak K.],
Partial Binarization of Neural Networks for Budget-Aware Efficient
Learning,
WACV24(2325-2334)
IEEE DOI
2404
Systematics, Neural networks, Object tracking, Iterative methods,
Algorithms, Machine learning architectures, formulations, and algorithms
BibRef
van den Dool, W.[Winfried],
Blankevoort, T.[Tijmen],
Welling, M.[Max],
Asano, Y.M.[Yuki M.],
Efficient Neural PDE-Solvers using Quantization Aware Training,
REDLCV23(1415-1424)
IEEE DOI
2401
BibRef
Abati, D.[Davide],
Ben Yahia, H.[Haitam],
Nagel, M.[Markus],
Habibian, A.[Amirhossein],
ResQ: Residual Quantization for Video Perception,
ICCV23(17073-17083)
IEEE DOI
2401
BibRef
Chauhan, A.[Arun],
Tiwari, U.[Utsav],
Vikram, N.R.,
Post Training Mixed Precision Quantization of Neural Networks using
First-Order Information,
REDLCV23(1335-1344)
IEEE DOI
2401
BibRef
Pandey, N.P.[Nilesh Prasad],
Fournarakis, M.[Marios],
Patel, C.[Chirag],
Nagel, M.[Markus],
Softmax Bias Correction for Quantized Generative Models,
REDLCV23(1445-1450)
IEEE DOI
2401
BibRef
Shang, Y.Z.[Yu-Zhang],
Xu, B.X.[Bing-Xin],
Liu, G.[Gaowen],
Kompella, R.R.[Ramana Rao],
Yan, Y.[Yan],
Causal-DFQ: Causality Guided Data-free Network Quantization,
ICCV23(17391-17400)
IEEE DOI
2401
BibRef
Xu, K.[Ke],
Han, L.[Lei],
Tian, Y.[Ye],
Yang, S.S.[Shang-Shang],
Zhang, X.Y.[Xing-Yi],
EQ-Net: Elastic Quantization Neural Networks,
ICCV23(1505-1514)
IEEE DOI Code:
WWW Link.
2401
BibRef
Wu, H.M.[Hui-Min],
Lei, C.Y.[Chen-Yang],
Sun, X.[Xiao],
Wang, P.S.[Peng-Shuai],
Chen, Q.F.[Qi-Feng],
Cheng, K.T.[Kwang-Ting],
Lin, S.[Stephen],
Wu, Z.R.[Zhi-Rong],
Randomized Quantization: A Generic Augmentation for Data Agnostic
Self-supervised Learning,
ICCV23(16259-16270)
IEEE DOI Code:
WWW Link.
2401
BibRef
Li, T.X.[Tian-Xiang],
Chen, B.[Bin],
Wang, Q.W.[Qian-Wei],
Huang, Y.J.[Yu-Jun],
Xia, S.T.[Shu-Tao],
LKBQ: Pushing the Limit of Post-Training Quantization to Extreme 1
bit,
ICIP23(1775-1779)
IEEE DOI
2312
BibRef
Yvinec, E.[Edouard],
Dapogny, A.[Arnaud],
Bailly, K.[Kevin],
Designing Strong Baselines for Ternary Neural Network Quantization
through Support and Mass Equalization,
ICIP23(540-544)
IEEE DOI
2312
BibRef
Jeon, Y.[Yongkweon],
Lee, C.[Chungman],
Kim, H.Y.[Ho-Young],
Genie: Show Me the Data for Quantization,
CVPR23(12064-12073)
IEEE DOI
2309
BibRef
Liu, J.W.[Jia-Wei],
Niu, L.[Lin],
Yuan, Z.H.[Zhi-Hang],
Yang, D.W.[Da-Wei],
Wang, X.G.[Xing-Gang],
Liu, W.Y.[Wen-Yu],
PD-Quant: Post-Training Quantization Based on Prediction Difference
Metric,
CVPR23(24427-24437)
IEEE DOI
2309
BibRef
Noh, H.C.[Hae-Chan],
Hyun, S.[Sangeek],
Jeong, W.[Woojin],
Lim, H.S.[Han-Shin],
Heo, J.P.[Jae-Pil],
Disentangled Representation Learning for Unsupervised Neural
Quantization,
CVPR23(12001-12010)
IEEE DOI
2309
BibRef
Eliezer, N.S.[Nurit Spingarn],
Banner, R.[Ron],
Ben-Yaakov, H.[Hilla],
Hoffer, E.[Elad],
Michaeli, T.[Tomer],
Power Awareness in Low Precision Neural Networks,
CADK22(67-83).
Springer DOI
2304
BibRef
Finkelstein, A.[Alex],
Fuchs, E.[Ella],
Tal, I.[Idan],
Grobman, M.[Mark],
Vosco, N.[Niv],
Meller, E.[Eldad],
QFT: Post-training Quantization via Fast Joint Finetuning of All
Degrees of Freedom,
CADK22(115-129).
Springer DOI
2304
BibRef
Ben-Moshe, L.[Lior],
Benaim, S.[Sagie],
Wolf, L.B.[Lior B.],
FewGAN: Generating from the Joint Distribution of a Few Images,
ICIP22(751-755)
IEEE DOI
2211
Training, Quantization (signal), Image coding, Semantics,
Task analysis, GANs, Few-Shot learning, Quantization
BibRef
Tonin, M.[Marcos],
de Queiroz, R.L.[Ricardo L.],
On Quantization of Image Classification Neural Networks for
Compression Without Retraining,
ICIP22(916-920)
IEEE DOI
2211
Quantization (signal), Image coding, Laplace equations,
Transform coding, Artificial neural networks, Entropy, Standards,
ONNX file compression
BibRef
Cao, Y.H.[Yun-Hao],
Sun, P.Q.[Pei-Qin],
Huang, Y.C.[Ye-Chang],
Wu, J.X.[Jian-Xin],
Zhou, S.C.[Shu-Chang],
Synergistic Self-supervised and Quantization Learning,
ECCV22(XXX:587-604).
Springer DOI
2211
BibRef
Zhu, Y.[Ye],
Olszewski, K.[Kyle],
Wu, Y.[Yu],
Achlioptas, P.[Panos],
Chai, M.L.[Meng-Lei],
Yan, Y.[Yan],
Tulyakov, S.[Sergey],
Quantized GAN for Complex Music Generation from Dance Videos,
ECCV22(XXXVII:182-199).
Springer DOI
2211
BibRef
Oh, S.[Sangyun],
Sim, H.[Hyeonuk],
Kim, J.[Jounghyun],
Lee, J.[Jongeun],
Non-uniform Step Size Quantization for Accurate Post-training
Quantization,
ECCV22(XI:658-673).
Springer DOI
2211
BibRef
Chikin, V.[Vladimir],
Solodskikh, K.[Kirill],
Zhelavskaya, I.[Irina],
Explicit Model Size Control and Relaxation via Smooth Regularization
for Mixed-Precision Quantization,
ECCV22(XII:1-16).
Springer DOI
2211
BibRef
Kim, H.B.[Han-Byul],
Park, E.[Eunhyeok],
Yoo, S.[Sungjoo],
BASQ: Branch-wise Activation-clipping Search Quantization for Sub-4-bit
Neural Networks,
ECCV22(XII:17-33).
Springer DOI
2211
BibRef
Youn, J.[Jiseok],
Song, J.H.[Jae-Hun],
Kim, H.S.[Hyung-Sin],
Bahk, S.[Saewoong],
Bitwidth-Adaptive Quantization-Aware Neural Network Training:
A Meta-Learning Approach,
ECCV22(XII:208-224).
Springer DOI
2211
BibRef
Tang, C.[Chen],
Ouyang, K.[Kai],
Wang, Z.[Zhi],
Zhu, Y.F.[Yi-Fei],
Ji, W.[Wen],
Wang, Y.W.[Yao-Wei],
Zhu, W.W.[Wen-Wu],
Mixed-Precision Neural Network Quantization via Learned Layer-Wise
Importance,
ECCV22(XI:259-275).
Springer DOI
2211
BibRef
Solodskikh, K.[Kirill],
Chikin, V.[Vladimir],
Aydarkhanov, R.[Ruslan],
Song, D.H.[De-Hua],
Zhelavskaya, I.[Irina],
Wei, J.S.[Jian-Sheng],
Towards Accurate Network Quantization with Equivalent Smooth
Regularizer,
ECCV22(XI:727-742).
Springer DOI
2211
BibRef
Jin, G.J.[Gao-Jie],
Yi, X.P.[Xin-Ping],
Huang, W.[Wei],
Schewe, S.[Sven],
Huang, X.W.[Xiao-Wei],
Enhancing Adversarial Training with Second-Order Statistics of Weights,
CVPR22(15252-15262)
IEEE DOI
2210
Training, Deep learning, Correlation, Perturbation methods,
Neural networks, Estimation, Robustness, Optimization methods
BibRef
Zhu, X.S.[Xiao-Su],
Song, J.K.[Jing-Kuan],
Gao, L.L.[Lian-Li],
Zheng, F.[Feng],
Shen, H.T.[Heng Tao],
Unified Multivariate Gaussian Mixture for Efficient Neural Image
Compression,
CVPR22(17591-17600)
IEEE DOI
2210
Visualization, Image coding, Codes, Vector quantization, Redundancy,
Rate-distortion, Rate distortion theory, Low-level vision,
Representation learning
BibRef
Zhong, Y.S.[Yun-Shan],
Lin, M.B.[Ming-Bao],
Nan, G.R.[Gong-Rui],
Liu, J.Z.[Jian-Zhuang],
Zhang, B.C.[Bao-Chang],
Tian, Y.H.[Yong-Hong],
Ji, R.R.[Rong-Rong],
IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for
Zero-Shot Network Quantization,
CVPR22(12329-12338)
IEEE DOI
2210
Technological innovation, Quantization (signal), Codes,
Computational modeling, Neural networks,
Efficient learning and inferences
BibRef
Liu, Z.H.[Zhen-Hua],
Wang, Y.H.[Yun-He],
Han, K.[Kai],
Ma, S.W.[Si-Wei],
Gao, W.[Wen],
Instance-Aware Dynamic Neural Network Quantization,
CVPR22(12424-12433)
IEEE DOI
2210
Deep learning, Quantization (signal), Image recognition, Costs,
Neural networks, Termination of employment, Network architecture,
Deep learning architectures and techniques
BibRef
Chikin, V.[Vladimir],
Kryzhanovskiy, V.[Vladimir],
Channel Balancing for Accurate Quantization of Winograd Convolutions,
CVPR22(12497-12506)
IEEE DOI
2210
Training, Deep learning, Quantization (signal), Tensors, Convolution,
Optimization methods, Filtering algorithms
BibRef
Pandey, D.S.[Deep Shankar],
Yu, Q.[Qi],
Multidimensional Belief Quantification for Label-Efficient
Meta-Learning,
CVPR22(14371-14380)
IEEE DOI
2210
Training, Uncertainty, Computational modeling,
Measurement uncertainty, Predictive models,
Self- semi- meta- unsupervised learning
BibRef
Liu, H.Y.[Hong-Yang],
Elkerdawy, S.[Sara],
Ray, N.[Nilanjan],
Elhoushi, M.[Mostafa],
Layer Importance Estimation with Imprinting for Neural Network
Quantization,
MAI21(2408-2417)
IEEE DOI
2109
Training, Quantization (signal), Search methods, Neural networks,
Estimation, Reinforcement learning
BibRef
Yun, S.[Stone],
Wong, A.[Alexander],
Do All MobileNets Quantize Poorly? Gaining Insights into the Effect
of Quantization on Depthwise Separable Convolutional Networks Through
the Eyes of Multi-scale Distributional Dynamics,
MAI21(2447-2456)
IEEE DOI
2109
Degradation, Training, Quantization (signal), Systematics,
Fluctuations, Dynamic range, Robustness
BibRef
Fournarakis, M.[Marios],
Nagel, M.[Markus],
In-Hindsight Quantization Range Estimation for Quantized Training,
ECV21(3057-3064)
IEEE DOI
2109
Training, Quantization (signal), Tensors, Neural networks,
Estimation, Dynamic range, Benchmark testing
BibRef
Yu, H.C.[Hai-Chao],
Yang, L.J.[Lin-Jie],
Shi, H.[Humphrey],
Is In-Domain Data Really Needed? A Pilot Study on Cross-Domain
Calibration for Network Quantization,
ECV21(3037-3046)
IEEE DOI
2109
Knowledge engineering, Training, Quantization (signal),
Ultrasonic imaging, Sensitivity, Satellites, Calibration
BibRef
Langroudi, H.F.[Hamed F.],
Karia, V.[Vedant],
Carmichael, Z.[Zachariah],
Zyarah, A.[Abdullah],
Pandit, T.[Tej],
Gustafson, J.L.[John L.],
Kudithipudi, D.[Dhireesha],
Alps: Adaptive Quantization of Deep Neural Networks with GeneraLized
PositS,
ECV21(3094-3103)
IEEE DOI
2109
Deep learning, Quantization (signal), Adaptive systems,
Upper bound, Numerical analysis, Heuristic algorithms
BibRef
Abdolrashidi, A.[AmirAli],
Wang, L.[Lisa],
Agrawal, S.[Shivani],
Malmaud, J.[Jonathan],
Rybakov, O.[Oleg],
Leichner, C.[Chas],
Lew, L.[Lukasz],
Pareto-Optimal Quantized ResNet Is Mostly 4-bit,
ECV21(3085-3093)
IEEE DOI
2109
Training, Analytical models, Quantization (signal),
Computational modeling, Neural networks, Libraries, Hardware
BibRef
Trusov, A.[Anton],
Limonova, E.[Elena],
Slugin, D.[Dmitry],
Nikolaev, D.[Dmitry],
Arlazarov, V.V.[Vladimir V.],
Fast Implementation of 4-bit Convolutional Neural Networks for Mobile
Devices,
ICPR21(9897-9903)
IEEE DOI
2105
Performance evaluation, Quantization (signal), Neural networks,
Time measurement, Real-time systems, convolutional neural networks
BibRef
Hacene, G.B.[Ghouthi Boukli],
Lassance, C.[Carlos],
Gripon, V.[Vincent],
Courbariaux, M.[Matthieu],
Bengio, Y.[Yoshua],
Attention Based Pruning for Shift Networks,
ICPR21(4054-4061)
IEEE DOI
2105
Deep learning, Training, Quantization (signal), Convolution,
Transforms, Complexity theory
BibRef
Hou, Z.[Zejiang],
Kung, S.Y.[Sun-Yuan],
A Discriminant Information Approach to Deep Neural Network Pruning,
ICPR21(9553-9560)
IEEE DOI
2105
Quantization (signal), Power measurement, Image coding,
Neural networks, Tools, Benchmark testing
BibRef
Marinó, G.C.[Giosuè Cataldo],
Ghidoli, G.[Gregorio],
Frasca, M.[Marco],
Malchiodi, D.[Dario],
Compression strategies and space-conscious representations for deep
neural networks,
ICPR21(9835-9842)
IEEE DOI
2105
Quantization (signal), Source coding, Computational modeling,
Neural networks, Random access memory, Probabilistic logic,
drug-target prediction
BibRef
Yuan, Y.[Yong],
Chen, C.[Chen],
Hu, X.[Xiyuan],
Peng, S.[Silong],
Towards Low-Bit Quantization of Deep Neural Networks with Limited
Data,
ICPR21(4377-4384)
IEEE DOI
2105
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ICIP20(36-40)
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2011
Quantization (signal), Indexes, Neural networks, Context modeling,
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Sparse matrices, Decoding, Quantization (signal),
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ICIP20(3070-3074)
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2011
Quantization (signal), Neural networks,
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ICIP20(241-245)
IEEE DOI
2011
Neural networks, Quantization (signal), Training,
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CVPR20(13166-13175)
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2008
Quantization (signal), Training, Computational modeling,
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Adaptive Loss-Aware Quantization for Multi-Bit Networks,
CVPR20(7985-7994)
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Quantization (signal), Optimization, Neural networks,
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AdaBits: Neural Network Quantization With Adaptive Bit-Widths,
CVPR20(2143-2153)
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2008
Adaptation models, Quantization (signal), Training,
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CVPR20(1966-1976)
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2008
Training, Quantization (signal), Convergence, Acceleration,
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Training, Quantization (signal), Object detection, Detectors,
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Low-bit Quantization Needs Good Distribution,
EDLCV20(2909-2918)
IEEE DOI
2008
Quantization (signal), Training, Task analysis, Pipelines,
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EDLCV20(2978-2985)
IEEE DOI
2008
Quantization (signal), Training, Clamps, Neural networks,
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EDLCV20(2986-2996)
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2008
Quantization (signal), Computational modeling, Optimization,
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EDLCV20(3036-3046)
IEEE DOI
2008
Convolution, Quantization (signal),
Neural networks, Throughput, Hardware, Computational modeling
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APQ: Joint Search for Network Architecture, Pruning and Quantization
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CVPR20(2075-2084)
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2008
Quantization (signal), Optimization, Training, Hardware, Pipelines,
Biological system modeling, Computer architecture
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EDLCV20(3105-3113)
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2008
Neural networks, Quantization (signal), Mathematical model,
Computational modeling, Compounds, Entropy, Histograms
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Adaptive Posit: Parameter aware numerical format for deep learning
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EDLCV20(3123-3131)
IEEE DOI
2008
Dynamic range, Neural networks, Quantization (signal),
Computational modeling, Machine learning, Adaptation models, Numerical models
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Monte Carlo Gradient Quantization,
EDLCV20(3087-3095)
IEEE DOI
2008
Training, Quantization (signal), Monte Carlo methods, Convergence,
Neural networks, Heuristic algorithms, Image coding
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Dithered backprop: A sparse and quantized backpropagation algorithm
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EDLCV20(3096-3104)
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2008
Quantization (signal), Training, Mathematical model, Standards,
Neural networks, Convergence, Computational efficiency
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Structured Weight Unification and Encoding for Neural Network
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EDLCV20(3068-3076)
IEEE DOI
2008
Quantization (signal), Computational modeling, Encoding,
Image coding, Training, Acceleration, Predictive models
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Automatic Neural Network Compression by Sparsity-Quantization Joint
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CVPR20(2175-2185)
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2008
Quantization (signal), Optimization, Computational modeling,
Tensile stress, Search problems, Neural networks, Image coding
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Dong, Z.,
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Gholami, A.,
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HAWQ: Hessian AWare Quantization of Neural Networks With
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ICCV19(293-302)
IEEE DOI
2004
image resolution, neural nets, quantisation (signal),
neural networks, mixed-precision quantization, deep networks,
Image resolution
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2002
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A Fixed-Point Quantization Technique for Convolutional Neural
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ICIP19(3836-3840)
IEEE DOI
1910
CNNs, Fixed Point Quantization, Image Processing, Machine Vision, Deep Learning
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Ajanthan, T.,
Dokania, P.,
Hartley, R.,
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Proximal Mean-Field for Neural Network Quantization,
ICCV19(4870-4879)
IEEE DOI
2004
computational complexity, gradient methods, neural nets,
optimisation, stochastic processes, proximal mean-field, Labeling
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Gong, R.,
Liu, X.,
Jiang, S.,
Li, T.,
Hu, P.,
Lin, J.,
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Yan, J.,
Differentiable Soft Quantization:
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ICCV19(4851-4860)
IEEE DOI
2004
backpropagation, convolutional neural nets, data compression,
image coding, learning (artificial intelligence), Backpropagation
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Choukroun, Y.,
Kravchik, E.,
Yang, F.,
Kisilev, P.,
Low-bit Quantization of Neural Networks for Efficient Inference,
CEFRL19(3009-3018)
IEEE DOI
2004
inference mechanisms, learning (artificial intelligence),
mean square error methods, neural nets, quantisation (signal), MMSE
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Hu, Y.,
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Long, X.,
Hu, S.,
Zhu, J.,
Wang, X.,
Gu, Q.,
Cluster Regularized Quantization for Deep Networks Compression,
ICIP19(914-918)
IEEE DOI
1910
deep neural networks, object classification, model compression, quantization
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Manessi, F.,
Rozza, A.,
Bianco, S.,
Napoletano, P.,
Schettini, R.,
Automated Pruning for Deep Neural Network Compression,
ICPR18(657-664)
IEEE DOI
1812
Training, Neural networks, Quantization (signal), Task analysis,
Feature extraction, Pipelines, Image coding
BibRef
Faraone, J.,
Fraser, N.,
Blott, M.,
Leong, P.H.W.,
SYQ: Learning Symmetric Quantization for Efficient Deep Neural
Networks,
CVPR18(4300-4309)
IEEE DOI
1812
Quantization (signal), Hardware, Symmetric matrices, Training,
Complexity theory, Neural networks, Field programmable gate arrays
BibRef
Frickenstein, A.,
Unger, C.,
Stechele, W.,
Resource-Aware Optimization of DNNs for Embedded Applications,
CRV19(17-24)
IEEE DOI
1908
Optimization, Hardware, Computational modeling,
Quantization (signal), Training, Sensitivity, Autonomous vehicles, CNN
BibRef
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