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
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
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.[Wenwu],
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.[Xiaosu],
Song, J.[Jingkuan],
Gao, L.[Lianli],
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, Pattern recognition,
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, Pattern recognition,
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, Pattern recognition
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, Pattern recognition
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, Pattern recognition
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
Training, Quantization (signal), Sensitivity, Neural networks,
Object detection, Data models, Complexity theory
BibRef
Dbouk, H.[Hassan],
Sanghvi, H.[Hetul],
Mehendale, M.[Mahesh],
Shanbhag, N.[Naresh],
DBQ: A Differentiable Branch Quantizer for Lightweight Deep Neural
Networks,
ECCV20(XXVII:90-106).
Springer DOI
2011
BibRef
do Nascimento, M.G.[Marcelo Gennari],
Costain, T.W.[Theo W.],
Prisacariu, V.A.[Victor Adrian],
Finding Non-uniform Quantization Schemes Using Multi-task Gaussian
Processes,
ECCV20(XVII:383-398).
Springer DOI
2011
BibRef
Neumann, D.,
Sattler, F.,
Kirchhoffer, H.,
Wiedemann, S.,
Müller, K.,
Schwarz, H.,
Wiegand, T.,
Marpe, D.,
Samek, W.,
Deepcabac: Plug Play Compression of Neural Network Weights and Weight
Updates,
ICIP20(21-25)
IEEE DOI
2011
Artificial neural networks, Quantization (signal), Image coding,
Training, Servers, Compression algorithms, Neural Networks,
Distributed Training
BibRef
Haase, P.,
Schwarz, H.,
Kirchhoffer, H.,
Wiedemann, S.,
Marinc, T.,
Marban, A.,
Müller, K.,
Samek, W.,
Marpe, D.,
Wiegand, T.,
Dependent Scalar Quantization For Neural Network Compression,
ICIP20(36-40)
IEEE DOI
2011
Quantization (signal), Indexes, Neural networks, Context modeling,
Entropy coding, Image reconstruction,
neural network compression
BibRef
Kwon, S.J.,
Lee, D.,
Kim, B.,
Kapoor, P.,
Park, B.,
Wei, G.,
Structured Compression by Weight Encryption for Unstructured Pruning
and Quantization,
CVPR20(1906-1915)
IEEE DOI
2008
Sparse matrices, Decoding, Quantization (signal),
Viterbi algorithm, Bandwidth, Encryption
BibRef
Jung, J.,
Kim, J.,
Kim, Y.,
Kim, C.,
Reinforcement Learning-Based Layer-Wise Quantization For Lightweight
Deep Neural Networks,
ICIP20(3070-3074)
IEEE DOI
2011
Quantization (signal), Neural networks,
Learning (artificial intelligence), Computational modeling, Embedded system
BibRef
Geng, X.,
Lin, J.,
Li, S.,
Cascaded Mixed-Precision Networks,
ICIP20(241-245)
IEEE DOI
2011
Neural networks, Quantization (signal), Training,
Network architecture, Optimization, Image coding, Schedules,
Pruning
BibRef
Fang, J.[Jun],
Shafiee, A.[Ali],
Abdel-Aziz, H.[Hamzah],
Thorsley, D.[David],
Georgiadis, G.[Georgios],
Hassoun, J.H.[Joseph H.],
Post-training Piecewise Linear Quantization for Deep Neural Networks,
ECCV20(II:69-86).
Springer DOI
2011
BibRef
Xie, Z.[Zheng],
Wen, Z.Q.[Zhi-Quan],
Liu, J.[Jing],
Liu, Z.Q.[Zhi-Qiang],
Wu, X.X.[Xi-Xian],
Tan, M.K.[Ming-Kui],
Deep Transferring Quantization,
ECCV20(VIII:625-642).
Springer DOI
2011
BibRef
Wang, Y.[Ying],
Lu, Y.D.[Ya-Dong],
Blankevoort, T.[Tijmen],
Differentiable Joint Pruning and Quantization for Hardware Efficiency,
ECCV20(XXIX: 259-277).
Springer DOI
2010
BibRef
Cai, Y.H.[Yao-Hui],
Yao, Z.W.[Zhe-Wei],
Dong, Z.[Zhen],
Gholami, A.[Amir],
Mahoney, M.W.[Michael W.],
Keutzer, K.[Kurt],
ZeroQ: A Novel Zero Shot Quantization Framework,
CVPR20(13166-13175)
IEEE DOI
2008
Quantization (signal), Training, Computational modeling,
Sensitivity, Artificial neural networks, Task analysis, Training data
BibRef
Qu, Z.,
Zhou, Z.,
Cheng, Y.,
Thiele, L.,
Adaptive Loss-Aware Quantization for Multi-Bit Networks,
CVPR20(7985-7994)
IEEE DOI
2008
Quantization (signal), Optimization, Neural networks,
Adaptive systems, Microprocessors, Training, Tensile stress
BibRef
Jin, Q.,
Yang, L.,
Liao, Z.,
AdaBits: Neural Network Quantization With Adaptive Bit-Widths,
CVPR20(2143-2153)
IEEE DOI
2008
Adaptation models, Quantization (signal), Training,
Neural networks, Biological system modeling,
Adaptive systems
BibRef
Zhu, F.[Feng],
Gong, R.H.[Rui-Hao],
Yu, F.W.[Feng-Wei],
Liu, X.L.[Xiang-Long],
Wang, Y.F.[Yan-Fei],
Li, Z.L.[Zhe-Long],
Yang, X.Q.[Xiu-Qi],
Yan, J.J.[Jun-Jie],
Towards Unified INT8 Training for Convolutional Neural Network,
CVPR20(1966-1976)
IEEE DOI
2008
Training, Quantization (signal), Convergence, Acceleration,
Computer crashes, Optimization, Task analysis
BibRef
Zhuang, B.,
Liu, L.,
Tan, M.,
Shen, C.,
Reid, I.D.,
Training Quantized Neural Networks With a Full-Precision Auxiliary
Module,
CVPR20(1485-1494)
IEEE DOI
2008
Training, Quantization (signal), Object detection, Detectors,
Computational modeling, Task analysis, Neural networks
BibRef
Yu, H.,
Wen, T.,
Cheng, G.,
Sun, J.,
Han, Q.,
Shi, J.,
Low-bit Quantization Needs Good Distribution,
EDLCV20(2909-2918)
IEEE DOI
2008
Quantization (signal), Training, Task analysis, Pipelines,
Adaptation models, Computational modeling, Neural networks
BibRef
Bhalgat, Y.,
Lee, J.,
Nagel, M.,
Blankevoort, T.,
Kwak, N.,
LSQ+: Improving low-bit quantization through learnable offsets and
better initialization,
EDLCV20(2978-2985)
IEEE DOI
2008
Quantization (signal), Training, Clamps, Neural networks,
Artificial intelligence, Minimization
BibRef
Pouransari, H.,
Tu, Z.,
Tuzel, O.,
Least squares binary quantization of neural networks,
EDLCV20(2986-2996)
IEEE DOI
2008
Quantization (signal), Computational modeling, Optimization,
Tensile stress, Neural networks, Computational efficiency,
Approximation algorithms
BibRef
Gope, D.,
Beu, J.,
Thakker, U.,
Mattina, M.,
Ternary MobileNets via Per-Layer Hybrid Filter Banks,
EDLCV20(3036-3046)
IEEE DOI
2008
Convolution, Quantization (signal),
Neural networks, Throughput, Hardware, Computational modeling
BibRef
Wang, T.,
Wang, K.,
Cai, H.,
Lin, J.,
Liu, Z.,
Wang, H.,
Lin, Y.,
Han, S.,
APQ: Joint Search for Network Architecture, Pruning and Quantization
Policy,
CVPR20(2075-2084)
IEEE DOI
2008
Quantization (signal), Optimization, Training, Hardware, Pipelines,
Biological system modeling, Computer architecture
BibRef
Yu, H.B.[Hai-Bao],
Han, Q.[Qi],
Li, J.B.[Jian-Bo],
Shi, J.P.[Jian-Ping],
Cheng, G.L.[Guang-Liang],
Fan, B.[Bin],
Search What You Want:
Barrier Penalty NAS for Mixed Precision Quantization,
ECCV20(IX:1-16).
Springer DOI
2011
BibRef
Marban, A.[Arturo],
Becking, D.[Daniel],
Wiedemann, S.[Simon],
Samek, W.[Wojciech],
Learning Sparse Ternary Neural Networks with Entropy-Constrained
Trained Ternarization (EC2T),
EDLCV20(3105-3113)
IEEE DOI
2008
Neural networks, Quantization (signal), Mathematical model,
Computational modeling, Compounds, Entropy, Histograms
BibRef
Langroudi, H.F.[Hamed F.],
Karia, V.[Vedant],
Gustafson, J.L.[John L.],
Kudithipudi, D.[Dhireesha],
Adaptive Posit: Parameter aware numerical format for deep learning
inference on the edge,
EDLCV20(3123-3131)
IEEE DOI
2008
Dynamic range, Neural networks, Quantization (signal),
Computational modeling, Machine learning, Adaptation models, Numerical models
BibRef
Mordido, G.,
van Keirsbilck, M.,
Keller, A.,
Monte Carlo Gradient Quantization,
EDLCV20(3087-3095)
IEEE DOI
2008
Training, Quantization (signal), Monte Carlo methods, Convergence,
Neural networks, Heuristic algorithms, Image coding
BibRef
Wiedemann, S.,
Mehari, T.,
Kepp, K.,
Samek, W.,
Dithered backprop: A sparse and quantized backpropagation algorithm
for more efficient deep neural network training,
EDLCV20(3096-3104)
IEEE DOI
2008
Quantization (signal), Training, Mathematical model, Standards,
Neural networks, Convergence, Computational efficiency
BibRef
Jiang, W.,
Wang, W.,
Liu, S.,
Structured Weight Unification and Encoding for Neural Network
Compression and Acceleration,
EDLCV20(3068-3076)
IEEE DOI
2008
Quantization (signal), Computational modeling, Encoding,
Image coding, Training, Acceleration, Predictive models
BibRef
Yang, H.,
Gui, S.,
Zhu, Y.,
Liu, J.,
Automatic Neural Network Compression by Sparsity-Quantization Joint
Learning: A Constrained Optimization-Based Approach,
CVPR20(2175-2185)
IEEE DOI
2008
Quantization (signal), Optimization, Computational modeling,
Tensile stress, Search problems, Neural networks, Image coding
BibRef
Dong, Z.,
Yao, Z.,
Gholami, A.,
Mahoney, M.,
Keutzer, K.,
HAWQ: Hessian AWare Quantization of Neural Networks With
Mixed-Precision,
ICCV19(293-302)
IEEE DOI
2004
image resolution, neural nets, quantisation (signal),
neural networks, mixed-precision quantization, deep networks,
Image resolution
BibRef
Yang, J.[Jiwei],
Shen, X.[Xu],
Xing, J.[Jun],
Tian, X.M.[Xin-Mei],
Li, H.Q.A.[Hou-Qi-Ang],
Deng, B.[Bing],
Huang, J.Q.[Jian-Qiang],
Hua, X.S.[Xian-Sheng],
Quantization Networks,
CVPR19(7300-7308).
IEEE DOI
2002
BibRef
Cao, S.J.[Shi-Jie],
Ma, L.X.[Ling-Xiao],
Xiao, W.C.[Wen-Cong],
Zhang, C.[Chen],
Liu, Y.X.[Yun-Xin],
Zhang, L.T.[Lin-Tao],
Nie, L.S.[Lan-Shun],
Yang, Z.[Zhi],
SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity
Through Low-Bit Quantization,
CVPR19(11208-11217).
IEEE DOI
2002
BibRef
Jung, S.[Sangil],
Son, C.Y.[Chang-Yong],
Lee, S.[Seohyung],
Son, J.[Jinwoo],
Han, J.J.[Jae-Joon],
Kwak, Y.[Youngjun],
Hwang, S.J.[Sung Ju],
Choi, C.K.[Chang-Kyu],
Learning to Quantize Deep Networks by Optimizing Quantization Intervals
With Task Loss,
CVPR19(4345-4354).
IEEE DOI
2002
BibRef
Mitschke, N.,
Heizmann, M.,
Noffz, K.,
Wittmann, R.,
A Fixed-Point Quantization Technique for Convolutional Neural
Networks Based on Weight Scaling,
ICIP19(3836-3840)
IEEE DOI
1910
CNNs, Fixed Point Quantization, Image Processing, Machine Vision, Deep Learning
BibRef
Ajanthan, T.,
Dokania, P.,
Hartley, R.,
Torr, P.H.S.,
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
BibRef
Gong, R.,
Liu, X.,
Jiang, S.,
Li, T.,
Hu, P.,
Lin, J.,
Yu, F.,
Yan, J.,
Differentiable Soft Quantization:
Bridging Full-Precision and Low-Bit Neural Networks,
ICCV19(4851-4860)
IEEE DOI
2004
backpropagation, convolutional neural nets, data compression,
image coding, learning (artificial intelligence), Backpropagation
BibRef
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
BibRef
Hu, Y.,
Li, J.,
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
BibRef
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
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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
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Intrepretation, Explaination, Understanding of Convolutional Neural Networks .