14.5.8.6.7 Neural Net Compression

Chapter Contents (Back)
CNN. Compression. Efficient Implementation.
See also Neural Net Pruning.
See also Knowledge Distillation.

Tung, F.[Frederick], Mori, G.[Greg],
Deep Neural Network Compression by In-Parallel Pruning-Quantization,
PAMI(42), No. 3, March 2020, pp. 568-579.
IEEE DOI 2002
BibRef
Earlier:
CLIP-Q: Deep Network Compression Learning by In-parallel Pruning-Quantization,
CVPR18(7873-7882)
IEEE DOI 1812
Quantization (signal), Image coding, Neural networks, Visualization, Training, Convolution, Network architecture, Bayesian optimization. Training, Task analysis, Optimization BibRef

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, Computer vision, Network pruning, Network compressing, Top-down attention, Perceptual visioning 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

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

Bui, K.[Kevin], Park, F.[Fredrick], Zhang, S.[Shuai], Qi, Y.[Yingyong], Xin, J.[Jack],
Nonconvex Regularization for Network Slimming: Compressing CNNS Even More,
ISVC20(I:39-53).
Springer DOI 2103
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

Suzuki, S., Takagi, M., Takeda, S., Tanida, R., Kimata, H.,
Deep Feature Compression With Spatio-Temporal Arranging for Collaborative Intelligence,
ICIP20(3099-3103)
IEEE DOI 2011
Image coding, Correlation, Cloud computing, Quantization (signal), Image edge detection, Collaborative intelligence, spatio-temporal arranging 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

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

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

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

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

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

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

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Forgetting, Explaination, Intrepretation, Understanding of Convolutional Neural Networks .


Last update:Jun 14, 2021 at 09:20:36