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1506
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PAMI(38), No. 10, October 2016, pp. 1943-1955.
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1609
Acceleration
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Channel Pruning for Accelerating Very Deep Neural Networks,
ICCV17(1398-1406)
IEEE DOI
1802
iterative methods, learning (artificial intelligence),
least squares approximations, neural nets, regression analysis,
Training
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Zhang, X.Y.[Xiang-Yu],
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Ming, X.[Xiang],
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networks,
CVPR15(1984-1992)
IEEE DOI
1510
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He, K.M.[Kai-Ming],
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Ren, S.Q.[Shao-Qing],
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Delving Deep into Rectifiers:
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ICCV15(1026-1034)
IEEE DOI
1602
Adaptation models
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Efficient Processing of Deep Neural Networks: A Tutorial and Survey,
PIEEE(105), No. 12, December 2017, pp. 2295-2329.
IEEE DOI
1712
Survey, Deep Neural Networks. Artificial intelligence, Benchmark testing,
Biological neural networks, Computer architecture,
spatial architectures
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Cavigelli, L.,
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Origami: A 803-GOp/s/W Convolutional Network Accelerator,
CirSysVideo(27), No. 11, November 2017, pp. 2461-2475.
IEEE DOI
1712
Computer architecture, Feature extraction,
Machine learning, Mobile communication, Neural networks,
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Ghesu, F.C.[Florin C.],
Krubasik, E.,
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Marginal Space Deep Learning: Efficient Architecture for Volumetric
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1605
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Sun, B.,
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Efficient Compressed Sensing for Wireless Neural Recording:
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IEEE DOI
1705
Compressed sensing, Cost function, Dictionaries, Sensors, Training,
Wireless communication, Wireless sensor networks,
Compressed sensing (CS), deep neural network, wireless neural recording
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Xu, T.B.[Ting-Bing],
Yang, P.[Peipei],
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PR(88), 2019, pp. 272-284.
Elsevier DOI
1901
Deep network acceleration and compression,
Architecture distillation, Lightweight network
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Kim, D.H.[Dae Ha],
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IVC(87), 2019, pp. 68-75.
Elsevier DOI
1906
BibRef
Earlier: A1, A3, A4, Only:
MUNet: Macro Unit-Based Convolutional Neural Network for Mobile
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EfficientDeep18(1749-17498)
IEEE DOI
1812
Deep neural networks, Light-weight deep learning, Macro-unit.
Convolution, Computational complexity,
Mobile handsets, Neural networks, Performance evaluation
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Elsevier DOI
1909
Deep compression, Probabilistic graphical models,
Probabilistic graphical networks, Deep learning
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Brillet, L.F.,
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Tunable CNN Compression Through Dimensionality Reduction,
ICIP19(3851-3855)
IEEE DOI
1910
CNN, PCA, compression
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Lin, S.H.[Shao-Hui],
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Holistic CNN Compression via Low-Rank Decomposition with Knowledge
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PAMI(41), No. 12, December 2019, pp. 2889-2905.
IEEE DOI
1911
Knowledge transfer, Image coding, Task analysis,
Information exchange, Computational modeling, CNN acceleration
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Zhang, L.[Lin],
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2001
Visualization, Image coding, Task analysis, Feature extraction,
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Elsevier DOI
2002
Deep learning, Regularization, Group sparsity, Image categorization
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Joint Coding of Local and Global Deep Features in Videos for Visual
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IP(29), 2020, pp. 3734-3749.
IEEE DOI
2002
Local deep feature, joint coding, visual search, inter-feature correlation
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PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural
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DOI Link
2004
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Liu, Z.C.[Ze-Chun],
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Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance,
IJCV(128), No. 1, January 2020, pp. 202-219.
Springer DOI
2002
BibRef
Earlier: A1, A3, A2, A4, A5, A6:
Bi-Real Net: Enhancing the Performance of 1-Bit CNNs with Improved
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Springer DOI
1810
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Cavigelli, L.,
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CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional
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CirSysVideo(30), No. 5, May 2020, pp. 1451-1465.
IEEE DOI
2005
Learning with video.
Feature extraction, Object detection, Throughput, Convolution,
Inference algorithms, Semantics, Approximation algorithms,
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2005
ResNet to deal with temporal.
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Kalayeh, M.M.[Mahdi M.],
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Training Faster by Separating Modes of Variation in Batch-Normalized
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IEEE DOI
2005
Training, Kernel, Mathematical model, Transforms,
Probability density function, Statistics, Acceleration, fisher vector
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Ma, W.C.[Wen-Chi],
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PR(100), 2020, pp. 107149.
Elsevier DOI
2005
Deep feature learning, Multi-scale,
Semantic and contextual information, Small and occluded objects
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Elsevier DOI
2201
Image classification, Discriminative features, Channel attention mechanism
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Ma, W.C.[Wen-Chi],
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MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for
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ICPR18(2510-2515)
IEEE DOI
1812
Feature extraction, Object detection, Computational modeling,
Task analysis, Convolutional neural networks, Hardware, Real-time systems
BibRef
Lelekas, I.,
Tomen, N.,
Pintea, S.L.,
van Gemert, J.C.,
Top-Down Networks: A coarse-to-fine reimagination of CNNs,
DeepVision20(3244-3253)
IEEE DOI
2008
Feature extraction, Spatial resolution, Computer architecture,
Merging, Task analysis, Visualization, Robustness
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Ma, L.H.[Long-Hua],
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IET-IPR(14), No. 11, September 2020, pp. 2435-2441.
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2009
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Jiang, Y.G.[Yu-Gang],
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IP(29), 2020, pp. 8747-8759.
IEEE DOI
2009
Task analysis, Visualization, Computational modeling,
Biological information theory, Computational efficiency, Neurons,
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Fang, Z.Y.[Zhen-Yu],
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Topological optimization of the DenseNet with pretrained-weights
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PR(109), 2021, pp. 107608.
Elsevier DOI
2009
Deep convolutional neural networks, Genetic algorithms,
Parameter reduction, Structure optimization, DenseNet
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Li, G.Q.[Guo-Qing],
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Efficient densely connected convolutional neural networks,
PR(109), 2021, pp. 107610.
Elsevier DOI
2009
Convolutional neural networks, Classification,
Parameter efficiency, Densely connected
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Yang, Y.Q.[Yong-Quan],
Lv, H.J.[Hai-Jun],
Chen, N.[Ning],
Wu, Y.[Yang],
Zheng, J.[Jiayi],
Zheng, Z.X.[Zhong-Xi],
Local minima found in the subparameter space can be effective for
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PR(109), 2021, pp. 107582.
Elsevier DOI
2009
Ensemble learning, Ensemble selection, Ensemble fusion,
Deep convolutional neural network
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Gürhanli, A.[Ahmet],
Accelerating convolutional neural network training using ProMoD
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IET-IPR(14), No. 13, November 2020, pp. 2957-2964.
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2012
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Xi, J.B.[Jiang-Bo],
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Fang, J.W.[Jian-Wu],
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Wu, T.J.[Tian-Jun],
Zhao, C.Y.[Chao-Ying],
Li, Z.H.[Zhen-Hong],
Wide Sliding Window and Subsampling Network for Hyperspectral Image
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RS(13), No. 7, 2021, pp. xx-yy.
DOI Link
2104
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Xi, J.B.[Jiang-Bo],
Cong, M.[Ming],
Ersoy, O.K.[Okan K.],
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Zhao, C.Y.[Chao-Ying],
Li, Z.H.[Zhen-Hong],
Gu, J.K.[Jun-Kai],
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Dynamic Wide and Deep Neural Network for Hyperspectral Image
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RS(13), No. 13, 2021, pp. xx-yy.
DOI Link
2107
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Avola, D.[Danilo],
Cinque, L.[Luigi],
Diko, A.[Anxhelo],
Fagioli, A.[Alessio],
Foresti, G.L.[Gian Luca],
Mecca, A.[Alessio],
Pannone, D.[Daniele],
Piciarelli, C.[Claudio],
MS-Faster R-CNN: Multi-Stream Backbone for Improved Faster R-CNN
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RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
2105
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Cancela, B.[Brais],
Bolón-Canedo, V.[Verónica],
Alonso-Betanzos, A.[Amparo],
Can data placement be effective for Neural Networks classification
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ICPR21(392-399)
IEEE DOI
2105
Training, Neural networks, Tools, Pattern recognition,
Classification algorithms, Proposals, Task analysis
BibRef
Jie, Z.[Zequn],
Sun, P.[Peng],
Li, X.[Xin],
Feng, J.S.[Jia-Shi],
Liu, W.[Wei],
Anytime Recognition with Routing Convolutional Networks,
PAMI(43), No. 6, June 2021, pp. 1875-1886.
IEEE DOI
2106
Routing, Neural networks, Task analysis, Benchmark testing,
Computational modeling, Reinforcement learning,
semantic segmentation
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Koçanaogullari, A.[Aziz],
Smedemark-Margulies, N.[Niklas],
Akcakaya, M.[Murat],
Erdogmus, D.[Deniz],
Geometric Analysis of Uncertainty Sampling for Dense Neural Network
Layer,
SPLetters(28), 2021, pp. 867-871.
IEEE DOI
2106
Uncertainty, Sampling methods, Adaptation models, Neural networks,
Training, Geometry, Task analysis, Active learning,
uncertainty sampling
BibRef
Paoletti, M.E.[Mercedes E.],
Haut, J.M.[Juan M.],
Tao, X.[Xuanwen],
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Plaza, A.[Antonio],
FLOP-Reduction Through Memory Allocations Within CNN for
Hyperspectral Image Classification,
GeoRS(59), No. 7, July 2021, pp. 5938-5952.
IEEE DOI
2106
Computational modeling, Convolution, Data models,
Feature extraction, Kernel, Hyperspectral imaging, Classification,
shift operation
BibRef
Liu, X.Y.[Xin-Yu],
Di, X.G.[Xiao-Guang],
TanhExp: A smooth activation function with high convergence speed for
lightweight neural networks,
IET-CV(15), No. 2, 2021, pp. 136-150.
DOI Link
2106
BibRef
Gao, H.Y.[Hong-Yang],
Wang, Z.Y.[Zheng-Yang],
Cai, L.[Lei],
Ji, S.W.[Shui-Wang],
ChannelNets: Compact and Efficient Convolutional Neural Networks via
Channel-Wise Convolutions,
PAMI(43), No. 8, August 2021, pp. 2570-2581.
IEEE DOI
2107
Convolutional codes, Image coding, Computational modeling, Kernel,
Computational efficiency, Mobile handsets, Computer architecture,
model compression
BibRef
Hou, Y.[Yun],
Fan, H.[Hong],
Li, L.[Li],
Li, B.L.[Bai-Lin],
Adaptive learning cost-sensitive convolutional neural network,
IET-CV(15), No. 5, 2021, pp. 346-355.
DOI Link
2107
BibRef
Qiu, J.X.[Jia-Xiong],
Chen, C.[Cai],
Liu, S.C.[Shuai-Cheng],
Zhang, H.Y.[Heng-Yu],
Zeng, B.[Bing],
SlimConv: Reducing Channel Redundancy in Convolutional Neural
Networks by Features Recombining,
IP(30), 2021, pp. 6434-6445.
IEEE DOI
2107
Convolution, Computational modeling, Redundancy, Task analysis,
Kernel, Image reconstruction, Transforms, Slim convolution,
model compression
BibRef
Li, Z.Z.[Zheng-Ze],
Yang, X.Y.[Xiao-Yuan],
Shen, K.Q.[Kang-Qing],
Jiang, F.[Fazhen],
Jiang, J.[Jin],
Ren, H.[Huwei],
Li, Y.X.[Yi-Xiao],
PSGU: Parametric self-circulation gating unit for deep neural
networks,
JVCIR(80), 2021, pp. 103294.
Elsevier DOI
2110
Deep learning, Neural network, Activation function, PSGU, Initialization
BibRef
Han, Y.Z.[Yi-Zeng],
Huang, G.[Gao],
Song, S.[Shiji],
Yang, L.[Le],
Zhang, Y.T.[Yi-Tian],
Jiang, H.J.[Hao-Jun],
Spatially Adaptive Feature Refinement for Efficient Inference,
IP(30), 2021, pp. 9345-9358.
IEEE DOI
2112
Convolution, Spatial resolution, Redundancy, Adaptation models,
Computational modeling, Adaptive systems, Task analysis,
convolutional neural networks
BibRef
Chai, E.[Enhui],
Ta, L.[Lin],
Ma, Z.[Zhanfei],
Zhi, M.[Min],
ERF-YOLO:
A YOLO algorithm compatible with fewer parameters and higher accuracy,
IVC(116), 2021, pp. 104317.
Elsevier DOI
2112
The effective receptive field, The activation function,
The backbone network, Concat, The anchor box loss function
BibRef
Gong, S.J.[Shen-Jian],
Zhang, S.S.[Shan-Shan],
Yang, J.[Jian],
Yuen, P.C.[Pong Chi],
Self-Fusion Convolutional Neural Networks,
PRL(152), 2021, pp. 50-55.
Elsevier DOI
2112
Lightweight neural networks, Efficient feature fusion, Image classification
BibRef
Feng, J.W.[Jian-Wei],
Huang, D.[Dong],
Optimal Gradient Checkpoint Search for Arbitrary Computation Graphs,
CVPR21(11428-11437)
IEEE DOI
2111
Training, Costs, Tensors, Image resolution, Memory management,
Graphics processing units, Manuals
BibRef
Malinowski, M.[Mateusz],
Vytiniotis, D.[Dimitrios],
Swirszcz, G.[Grzegorz],
Patraucean, V.[Viorica],
Carreira, J.[João],
Gradient Forward-Propagation for Large-Scale Temporal Video Modelling,
CVPR21(9245-9255)
IEEE DOI
2111
Training, Couplings,
Computational modeling, Parallel processing, Streaming media, Feature extraction
BibRef
Ghodrati, A.[Amir],
Bejnordi, B.E.[Babak Ehteshami],
Habibian, A.[Amirhossein],
FrameExit: Conditional Early Exiting for Efficient Video Recognition,
CVPR21(15603-15613)
IEEE DOI
2111
Costs, Computational modeling, Logic gates,
Benchmark testing, Network architecture, Pattern recognition
BibRef
Li, H.D.[Heng-Duo],
Wu, Z.X.[Zu-Xuan],
Shrivastava, A.[Abhinav],
Davis, L.S.[Larry S.],
2D or not 2D? Adaptive 3D Convolution Selection for Efficient Video
Recognition,
CVPR21(6151-6160)
IEEE DOI
2111
Solid modeling, Gradient methods, Computational modeling, Predictive models
BibRef
Zhou, X.[Xiao],
Zhang, W.[Weizhong],
Xu, H.[Hang],
Zhang, T.[Tong],
Effective Sparsification of Neural Networks with Global Sparsity
Constraint,
CVPR21(3598-3607)
IEEE DOI
2111
Weight measurement, Training, Neural networks, Redundancy, Manuals,
Tools, Probabilistic logic
BibRef
Zhang, M.[Mingda],
Chu, C.T.[Chun-Te],
Zhmoginov, A.[Andrey],
Howard, A.[Andrew],
Jou, B.[Brendan],
Zhu, Y.K.[Yu-Kun],
Zhang, L.[Li],
Hwa, R.[Rebecca],
Kovashka, A.[Adriana],
BasisNet: Two-stage Model Synthesis for Efficient Inference,
ECV21(3075-3084)
IEEE DOI
2109
efficient neural network architectures, conditional computation, and
early termination.
Training, Computational modeling,
Neural networks, Computer architecture, Predictive models
BibRef
Zhang, C.[Chen],
Xu, Y.[Yinghao],
Shen, Y.J.[Yu-Jun],
CompConv: A Compact Convolution Module for Efficient Feature Learning,
ECV21(3006-3015)
IEEE DOI
2109
Convolution, Computational modeling,
Benchmark testing, Pattern recognition, Computational efficiency
BibRef
Chin, T.W.[Ting-Wu],
Marculescu, D.[Diana],
Morcos, A.S.[Ari S.],
Width transfer: on the (in)variance of width optimization,
ECV21(2984-2993)
IEEE DOI
2109
Training, Design methodology,
Training data, Optimization methods, Network architecture
BibRef
Yang, H.[Haojin],
Shen, Z.[Zhen],
Zhao, Y.C.[Yu-Cheng],
AsymmNet: Towards ultralight convolution neural networks using
asymmetrical bottlenecks,
MAI21(2339-2348)
IEEE DOI
2109
Convolutional codes,
Computational modeling, Neural networks, Computer architecture,
Pattern recognition
BibRef
Elhoushi, M.[Mostafa],
Chen, Z.[Zihao],
Shafiq, F.[Farhan],
Tian, Y.H.[Ye Henry],
Li, J.Y.[Joey Yiwei],
DeepShift: Towards Multiplication-Less Neural Networks,
MAI21(2359-2368)
IEEE DOI
2109
Training, Convolutional codes, Computational modeling,
Neural networks, Graphics processing units, Mobile handsets,
Pattern recognition
BibRef
Hong, M.F.[Min-Fong],
Chen, H.Y.[Hao-Yun],
Chen, M.H.[Min-Hung],
Xu, Y.S.[Yu-Syuan],
Kuo, H.K.[Hsien-Kai],
Tsai, Y.M.[Yi-Min],
Chen, H.J.[Hung-Jen],
Jou, K.[Kevin],
Network Space Search for Pareto-Efficient Spaces,
ECV21(3047-3056)
IEEE DOI
2109
Error analysis, Focusing, Manuals,
Computer architecture, Search problems
BibRef
Kim, K.[Kyungmin],
Wu, B.[Bichen],
Dai, X.L.[Xiao-Liang],
Zhang, P.[Peizhao],
Yan, Z.C.[Zhi-Cheng],
Vajda, P.[Peter],
Kim, S.[Seon],
Rethinking the Self-Attention in Vision Transformers,
ECV21(3065-3069)
IEEE DOI
2109
Computational modeling, Pattern recognition
BibRef
Lou, W.[Wei],
Xun, L.[Lei],
Sabet, A.[Amin],
Bi, J.[Jia],
Hare, J.[Jonathon],
Merrett, G.V.[Geoff V.],
Dynamic-OFA: Runtime DNN Architecture Switching for Performance
Scaling on Heterogeneous Embedded Platforms,
ECV21(3104-3112)
IEEE DOI
2109
Training, Computational modeling,
Pipelines, Graphics processing units, Computer architecture, Switches
BibRef
Avalos-López, J.I.[Jorge Ivan],
Rojas-Domínguez, A.[Alfonso],
Ornelas-Rodríguez, M.[Manuel],
Carpio, M.[Martín],
Valdez, S.I.[S. Ivvan],
Efficient Training of Deep Learning Models Through Improved Adaptive
Sampling,
MCPR21(141-152).
Springer DOI
2108
BibRef
Cai, S.F.[Shao-Feng],
Shu, Y.[Yao],
Wang, W.[Wei],
Dynamic Routing Networks,
WACV21(3587-3596)
IEEE DOI
2106
Training, Visualization, Computational modeling,
Neural networks, Computer architecture
BibRef
Ikami, D.[Daiki],
Irie, G.[Go],
Shibata, T.[Takashi],
Constrained Weight Optimization for Learning without Activation
Normalization,
WACV21(2605-2613)
IEEE DOI
2106
Deep learning, Perturbation methods,
MIMICs, Benchmark testing, Explosions
BibRef
Zhang, Y.[Yu],
Wu, X.Y.[Xiao-Yu],
Zhu, R.[Ruolin],
Adaptive Word Embedding Module for Semantic Reasoning in Large-scale
Detection,
ICPR21(2103-2109)
IEEE DOI
2105
External semantic information for CNNs.
Adaptive systems, Annotations, Image edge detection, Semantics,
Object detection, Cognition, object detection,
knowledge transfer
BibRef
Barlaud, M.[Michel],
Guyard, F.[Frédéric],
Learning sparse deep neural networks using efficient structured
projections on convex constraints for green AI,
ICPR21(1566-1573)
IEEE DOI
2105
Training, Gradient methods, Neural networks,
Computational efficiency, Projection algorithms, Artificial intelligence
BibRef
Chitsaz, K.[Kamran],
Hajabdollahi, M.[Mohsen],
Khadivi, P.[Pejman],
Samavi, S.[Shadrokh],
Karimi, N.[Nader],
Shirani, S.[Shahram],
Use of Frequency Domain for Complexity Reduction of Convolutional
Neural Networks,
MLCSA20(64-74).
Springer DOI
2103
BibRef
Berthelier, A.[Anthony],
Yan, Y.Z.[Yong-Zhe],
Chateau, T.[Thierry],
Blanc, C.[Christophe],
Duffner, S.[Stefan],
Garcia, C.[Christophe],
Learning Sparse Filters in Deep Convolutional Neural Networks with a
L1/l2 Pseudo-norm,
CADL20(662-676).
Springer DOI
2103
BibRef
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VCIP20(38-41)
IEEE DOI
2102
Servers, Data models, Collaborative work, Uplink, Urban areas,
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Zhang, L.,
Two recent advances on normalization methods for deep neural network
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VCIP20(1-1)
IEEE DOI
2102
Training, Optimization, Neural networks, Standardization,
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Jiang, Z.X.[Zi-Xuan],
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An Efficient Training Framework for Reversible Neural Architectures,
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2011
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2011
Executing some layers, pruning, etc.
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Isikdogan, L.F.[Leo F.],
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Michael, G.[Gilad],
SemifreddoNets: Partially Frozen Neural Networks for Efficient Computer
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2011
Partial frozen weights. Only change some weights in learning.
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Xie, X.,
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Exploring Highly Efficient Compact Neural Networks For Image
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ICIP20(2930-2934)
IEEE DOI
2011
Convolution, Standards, Neural networks, Computational efficiency,
Task analysis, Computer architecture, Fuses, Lightweight network,
inter-group information exchange
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Ahn, S.,
Chang, J.W.,
Kang, S.J.,
An Efficient Accelerator Design Methodology For Deformable
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ICIP20(3075-3079)
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2011
IP networks, Erbium, Zirconium, Indexes, Hardware accelerator,
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Code, CNN.
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Mining Self-similarity: Label Super-resolution with Epitomic
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2011
Learn from very large data-sets.
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Efficient Residue Number System Based Winograd Convolution,
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2011
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Profit: A Novel Training Method for sub-4-bit Mobilenet Models,
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2011
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Han, B.[Bing],
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Deep Spiking Neural Network: Energy Efficiency Through Time Based
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2011
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2011
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Dynamic Group Convolution for Accelerating Convolutional Neural
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ECCV20(VI:138-155).
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2011
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Lin, S.[Stephen],
Spatially Adaptive Inference with Stochastic Feature Sampling and
Interpolation,
ECCV20(I:531-548).
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2011
Reduci superfluous computation in feature maps of CNNs.
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Phan, A.H.[Anh-Huy],
Sobolev, K.[Konstantin],
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Tichavský, P.[Petr],
Glukhov, V.[Valeriy],
Oseledets, I.[Ivan],
Cichocki, A.[Andrzej],
Stable Low-rank Tensor Decomposition for Compression of Convolutional
Neural Network,
ECCV20(XXIX: 522-539).
Springer DOI
2010
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Yong, H.W.[Hong-Wei],
Huang, J.Q.[Jian-Qiang],
Hua, X.S.[Xian-Sheng],
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Gradient Centralization: A New Optimization Technique for Deep Neural
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ECCV20(I:635-652).
Springer DOI
2011
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Yuan, Z.N.[Zhuo-Ning],
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Yu, X.T.[Xiao-Tian],
Wang, X.Y.[Xiao-Yu],
Yang, T.B.[Tian-Bao],
Accelerating Deep Learning with Millions of Classes,
ECCV20(XXIII:711-726).
Springer DOI
2011
BibRef
Yang, T.J.[Tao-Jiannan],
Zhu, S.J.[Si-Jie],
Chen, C.[Chen],
Yan, S.[Shen],
Zhang, M.[Mi],
Willis, A.[Andrew],
MutualNet: Adaptive Convnet via Mutual Learning from Network Width and
Resolution,
ECCV20(I:299-315).
Springer DOI
2011
Code, ConvNet.
WWW Link. Executable with dynamic resources.
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Vu, T.[Thanh],
Eder, M.[Marc],
Price, T.[True],
Frahm, J.M.[Jan-Michael],
Any-Width Networks,
EDLCV20(3018-3026)
IEEE DOI
2008
Adjust width as needed.
Training, Switches, Computer architecture, Standards, Convolution,
Inference algorithms
BibRef
Elsen, E.,
Dukhan, M.,
Gale, T.,
Simonyan, K.,
Fast Sparse ConvNets,
CVPR20(14617-14626)
IEEE DOI
2008
Kernel, Computer architecture, Sparse matrices, Neural networks,
Standards, Computational modeling, Acceleration
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Pad, P.[Pedram],
Narduzzi, S.[Simon],
Kündig, C.[Clément],
Türetken, E.[Engin],
Bigdeli, S.A.[Siavash A.],
Dunbar, L.A.[L. Andrea],
Efficient Neural Vision Systems Based on Convolutional Image
Acquisition,
CVPR20(12282-12291)
IEEE DOI
2008
Optical imaging, Convolution, Optical sensors, Kernel,
Optical computing, Optical network units, Optical filters
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Song, G.L.[Guang-Lu],
Liu, Y.[Yu],
Wang, X.G.[Xiao-Gang],
Revisiting the Sibling Head in Object Detector,
CVPR20(11560-11569)
IEEE DOI
2008
in R-CNN.
Task analysis, Proposals, Detectors, Feature extraction, Training,
Google, Sensitivity
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Wang, Q.L.[Qi-Long],
Wu, B.G.[Bang-Gu],
Zhu, P.F.[Peng-Fei],
Li, P.H.[Pei-Hua],
Zuo, W.M.[Wang-Meng],
Hu, Q.H.[Qing-Hua],
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural
Networks,
CVPR20(11531-11539)
IEEE DOI
2008
Convolution, Complexity theory, Dimensionality reduction, Kernel,
Adaptation models, Computational modeling, Convolutional neural networks
BibRef
Chen, Y.P.[Yin-Peng],
Dai, X.Y.[Xi-Yang],
Liu, M.C.[Meng-Chen],
Chen, D.D.[Dong-Dong],
Yuan, L.[Lu],
Liu, Z.C.[Zi-Cheng],
Dynamic Convolution: Attention Over Convolution Kernels,
CVPR20(11027-11036)
IEEE DOI
2008
Expand as needed.
Convolution, Kernel, Computer architecture, Neural networks,
Computational efficiency, Computational modeling, Training
BibRef
Xie, Q.Z.[Qi-Zhe],
Luong, M.T.[Minh-Thang],
Hovy, E.[Eduard],
Le, Q.V.[Quoc V.],
Self-Training With Noisy Student Improves ImageNet Classification,
CVPR20(10684-10695)
IEEE DOI
2008
Noise measurement, Training, Stochastic processes, Robustness,
Entropy, Data models, Image resolution
BibRef
Verelst, T.[Thomas],
Tuytelaars, T.[Tinne],
Dynamic Convolutions: Exploiting Spatial Sparsity for Faster
Inference,
CVPR20(2317-2326)
IEEE DOI
2008
Graphics processing units, Task analysis, Neural networks,
Computer architecture, Tensile stress, Complexity theory, Image coding
BibRef
Goli, N.,
Aamodt, T.M.,
ReSprop: Reuse Sparsified Backpropagation,
CVPR20(1545-1555)
IEEE DOI
2008
Training, Convolution, Acceleration, Convolutional neural networks,
Hardware, Convergence, Correlation
BibRef
Idelbayev, Y.,
Carreira-Perpiñán, M.Á.,
Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer,
CVPR20(8046-8056)
IEEE DOI
2008
Neural networks, Training, Cost function, Tensile stress,
Image coding, Matrix decomposition
BibRef
Haroush, M.,
Hubara, I.,
Hoffer, E.,
Soudry, D.,
The Knowledge Within: Methods for Data-Free Model Compression,
CVPR20(8491-8499)
IEEE DOI
2008
Training, Data models, Optimization, Computational modeling,
Calibration, Training data, Degradation
BibRef
Rajagopal, A.[Aditya],
Bouganis, C.S.[Christos-Savvas],
perf4sight: A toolflow to model CNN training performance on Edge GPUs,
ERCVAD21(963-971)
IEEE DOI
2112
BibRef
Earlier:
Now that I can see, I can improve: Enabling data-driven finetuning of
CNNs on the edge,
EDLCV20(3058-3067)
IEEE DOI
2008
Training, Performance evaluation, Adaptation models, Power demand,
Network topology, Memory management, Predictive models.
Data models, Computational modeling, Topology
BibRef
Chatzikonstantinou, C.,
Papadopoulos, G.T.,
Dimitropoulos, K.,
Daras, P.,
Neural Network Compression Using Higher-Order Statistics and
Auxiliary Reconstruction Losses,
EDLCV20(3077-3086)
IEEE DOI
2008
Gaussian distribution, Training, Higher order statistics,
Measurement, Neural networks, Machine learning, Computational complexity
BibRef
Saini, R.,
Jha, N.K.,
Das, B.,
Mittal, S.,
Mohan, C.K.,
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact
Convolutional Neural Networks,
WACV20(1616-1625)
IEEE DOI
2006
Convolution, Computational modeling, Task analysis,
Computational efficiency, Feature extraction, Redundancy, Head
BibRef
Suau, X.,
Zappella, u.,
Apostoloff, N.,
Filter Distillation for Network Compression,
WACV20(3129-3138)
IEEE DOI
2006
Correlation, Training, Tensile stress,
Eigenvalues and eigenfunctions, Image coding, Decorrelation,
Principal component analysis
BibRef
Wang, M.,
Cai, H.,
Huang, X.,
Gong, M.,
ADNet: Adaptively Dense Convolutional Neural Networks,
WACV20(990-999)
IEEE DOI
2006
Adaptation models, Training, Convolution, Task analysis,
Computer architecture, Convolutional neural networks, Computational efficiency
BibRef
Hsu, L.,
Chiu, C.,
Lin, K.,
An Energy-Aware Bit-Serial Streaming Deep Convolutional Neural
Network Accelerator,
ICIP19(4609-4613)
IEEE DOI
1910
CNNs, Hardware Accelerator, EnergyAware, Precision, Bit-Serial PE,
Streaming Dataflow
BibRef
Lu, J.[Jing],
Xu, C.F.[Chao-Fan],
Zhang, W.[Wei],
Duan, L.Y.[Ling-Yu],
Mei, T.[Tao],
Sampling Wisely: Deep Image Embedding by Top-K Precision Optimization,
ICCV19(7960-7969)
IEEE DOI
2004
convolutional neural nets, gradient methods, image processing,
learning (artificial intelligence),
Toy manufacturing industry
BibRef
Nascimento, M.G.D.,
Prisacariu, V.,
Fawcett, R.,
DSConv: Efficient Convolution Operator,
ICCV19(5147-5156)
IEEE DOI
2004
convolutional neural nets, neural net architecture,
statistical distributions, DSConv,
Training data
BibRef
Chao, P.,
Kao, C.,
Ruan, Y.,
Huang, C.,
Lin, Y.,
HarDNet: A Low Memory Traffic Network,
ICCV19(3551-3560)
IEEE DOI
2004
feature extraction, image segmentation, neural nets,
object detection, neural network architectures, MACs,
Power demand
BibRef
Chen, Y.,
Fan, H.,
Xu, B.,
Yan, Z.,
Kalantidis, Y.,
Rohrbach, M.,
Shuicheng, Y.,
Feng, J.,
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural
Networks With Octave Convolution,
ICCV19(3434-3443)
IEEE DOI
2004
convolutional neural nets, feature extraction,
image classification, image resolution, neural net architecture, Kernel
BibRef
Phuong, M.[Mary],
Lampert, C.[Christoph],
Distillation-Based Training for Multi-Exit Architectures,
ICCV19(1355-1364)
IEEE DOI
2004
To terminate proessing early.
convolutional neural nets, image classification, probability,
supervised learning, training procedure, multiexit architectures,
BibRef
Chen, Y.,
Liu, S.,
Shen, X.,
Jia, J.,
Fast Point R-CNN,
ICCV19(9774-9783)
IEEE DOI
2004
convolutional neural nets, feature extraction,
image representation, object detection, solid modelling,
Detectors
BibRef
Gkioxari, G.,
Johnson, J.,
Malik, J.,
Mesh R-CNN,
ICCV19(9784-9794)
IEEE DOI
2004
computational geometry,
convolutional neural nets, feature extraction, graph theory,
Benchmark testing
BibRef
Vooturi, D.T.[Dharma Teja],
Varma, G.[Girish],
Kothapalli, K.[Kishore],
Dynamic Block Sparse Reparameterization of Convolutional Neural
Networks,
CEFRL19(3046-3053)
IEEE DOI
2004
Code, Convolutional Networks.
WWW Link. convolutional neural nets, image classification,
learning (artificial intelligence), dense neural networks, neural networks
BibRef
Gusak, J.,
Kholiavchenko, M.,
Ponomarev, E.,
Markeeva, L.,
Blagoveschensky, P.,
Cichocki, A.,
Oseledets, I.,
Automated Multi-Stage Compression of Neural Networks,
LPCV19(2501-2508)
IEEE DOI
2004
approximation theory, iterative methods, matrix decomposition,
neural nets, tensors, noniterative ones,
automated
BibRef
Hascoet, T.,
Febvre, Q.,
Zhuang, W.,
Ariki, Y.,
Takiguchi, T.,
Layer-Wise Invertibility for Extreme Memory Cost Reduction of CNN
Training,
NeruArch19(2049-2052)
IEEE DOI
2004
backpropagation, convolutional neural nets,
graphics processing units, minimal training memory consumption,
invertible transformations
BibRef
Ghosh, R.,
Gupta, A.K.,
Motani, M.,
Investigating Convolutional Neural Networks using Spatial Orderness,
NeruArch19(2053-2056)
IEEE DOI
2004
convolutional neural nets, image classification,
statistical analysis, convolutional neural networks, CNN,
Opening the black box of CNNs
BibRef
Cruz Vargas, J.A.[Jesus Adan],
Zamora Esquivel, J.[Julio],
Tickoo, O.[Omesh],
Introducing Region Pooling Learning,
CADL20(714-724).
Springer DOI
2103
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Esquivel, J.Z.[Julio Zamora],
Cruz Vargas, J.A.[Jesus Adan],
Tickoo, O.[Omesh],
Second Order Bifurcating Methodology for Neural Network Training and
Topology Optimization,
CADL20(725-738).
Springer DOI
2103
BibRef
Zamora Esquivel, J.,
Cruz Vargas, A.,
Lopez Meyer, P.,
Tickoo, O.,
Adaptive Convolutional Kernels,
NeruArch19(1998-2005)
IEEE DOI
2004
computational complexity,
convolutional neural nets, edge detection, feature extraction,
machine learning
BibRef
Köpüklü, O.,
Kose, N.,
Gunduz, A.,
Rigoll, G.,
Resource Efficient 3D Convolutional Neural Networks,
NeruArch19(1910-1919)
IEEE DOI
2004
convolutional neural nets, graphics processing units,
learning (artificial intelligence), UCF-101 dataset,
Action/Activity Recognition
BibRef
Yoo, K.M.,
Jo, H.S.,
Lee, H.,
Han, J.,
Lee, S.,
Stochastic Relational Network,
SDL-CV19(788-792)
IEEE DOI
2004
computational complexity, data visualisation,
inference mechanisms, learning (artificial intelligence),
gradient estimator
BibRef
Rannen-Triki, A.,
Berman, M.,
Kolmogorov, V.,
Blaschko, M.B.,
Function Norms for Neural Networks,
SDL-CV19(748-752)
IEEE DOI
2004
computational complexity, function approximation,
learning (artificial intelligence), neural nets,
Regularization
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Han, D.,
Yoo, H.,
Direct Feedback Alignment Based Convolutional Neural Network Training
for Low-Power Online Learning Processor,
LPCV19(2445-2452)
IEEE DOI
2004
backpropagation, convolutional neural nets,
learning (artificial intelligence), DFA algorithm, CNN training,
Back propagation
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Yan, X.,
Chen, Z.,
Xu, A.,
Wang, X.,
Liang, X.,
Lin, L.,
Meta R-CNN: Towards General Solver for Instance-Level Low-Shot
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ICCV19(9576-9585)
IEEE DOI
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Code, Learning.
HTML Version. convolutional neural nets, image representation,
image sampling, image segmentation, Object recognition
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Zhang, P.Z.[Pei-Zhao],
Wu, B.[Bichen],
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Sun, F.[Fei],
Wang, Y.[Yanghan],
Dukhan, M.[Marat],
Hu, Y.Q.[Yun-Qing],
Wu, Y.M.[Yi-Ming],
Jia, Y.Q.[Yang-Qing],
Vajda, P.[Peter],
Uyttendaele, M.[Matt],
Jha, N.K.[Niraj K.],
ChamNet: Towards Efficient Network Design Through Platform-Aware Model
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CVPR19(11390-11399).
IEEE DOI
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Gao, S.Q.[Shang-Qian],
Deng, C.[Cheng],
Huang, H.[Heng],
Cross Domain Model Compression by Structurally Weight Sharing,
CVPR19(8965-8974).
IEEE DOI
2002
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Liu, Y.J.[Ya-Jing],
Tian, X.M.[Xin-Mei],
Li, Y.[Ya],
Xiong, Z.W.[Zhi-Wei],
Wu, F.[Feng],
Compact Feature Learning for Multi-Domain Image Classification,
CVPR19(7186-7194).
IEEE DOI
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Li, J.[Jiashi],
Qi, Q.[Qi],
Wang, J.Y.[Jing-Yu],
Ge, C.[Ce],
Li, Y.J.[Yu-Jian],
Yue, Z.Z.[Zhang-Zhang],
Sun, H.F.[Hai-Feng],
OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural
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CVPR19(7039-7048).
IEEE DOI
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Kim, H.[Hyeji],
Khan, M.U.K.[Muhammad Umar Karim],
Kyung, C.M.[Chong-Min],
Efficient Neural Network Compression,
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IEEE DOI
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Cascaded Projection: End-To-End Network Compression and Acceleration,
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IEEE DOI
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Lin, Y.H.[Yu-Hsun],
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Chang, E.Y.[Edward Y.],
MBS: Macroblock Scaling for CNN Model Reduction,
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IEEE DOI
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Ma, J.[Jiayi],
Zhao, M.B.[Ming-Bo],
Liu, W.[Wei],
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NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural
Discriminative Dimensionality Reduction,
CVPR19(3200-3209).
IEEE DOI
2002
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Wang, H.Y.[Hui-Yu],
Kembhavi, A.[Aniruddha],
Farhadi, A.[Ali],
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Rastegari, M.[Mohammad],
ELASTIC: Improving CNNs With Dynamic Scaling Policies,
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IEEE DOI
2002
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Yang, H.C.[Hai-Chuan],
Zhu, Y.[Yuhao],
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ECC: Platform-Independent Energy-Constrained Deep Neural Network
Compression via a Bilinear Regression Model,
CVPR19(11198-11207).
IEEE DOI
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Gong, L.[Liyu],
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Exploiting Edge Features for Graph Neural Networks,
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ESPNetv2: A Light-Weight, Power Efficient, and General Purpose
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IEEE DOI
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T-Net: Parametrizing Fully Convolutional Nets With a Single High-Order
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Pu, S.L.[Shi-Liang],
All You Need Is a Few Shifts: Designing Efficient Convolutional Neural
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IEEE DOI
2002
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Georgiadis, G.[Georgios],
Accelerating Convolutional Neural Networks via Activation Map
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CVPR19(7078-7088).
IEEE DOI
2002
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Dong, X.[Xin],
Su, H.[Hao],
Binary Ensemble Neural Network: More Bits per Network or More Networks
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CVPR19(4918-4927).
IEEE DOI
2002
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Wu, B.Y.[Bao-Yuan],
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Compressing Convolutional Neural Networks via Factorized Convolutional
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Kim, E.[Eunwoo],
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Bag of Tricks for Image Classification with Convolutional Neural
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Fully Learnable Group Convolution for Acceleration of Deep Neural
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IEEE DOI
2002
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Li, Y.[Yuchao],
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Zhang, B.C.[Bao-Chang],
Liu, J.Z.[Jian-Zhuang],
Doermann, D.[David],
Wu, Y.[Yongjian],
Huang, F.Y.[Fei-Yue],
Ji, R.R.[Rong-Rong],
Exploiting Kernel Sparsity and Entropy for Interpretable CNN
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Building Efficient Deep Neural Networks With Unitary Group Convolutions,
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Neural Rejuvenation: Improving Deep Network Training by Enhancing
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High-Resolution Class Activation Mapping,
ICIP19(4514-4518)
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1910
Discriminative localization, Class Activation Map, Deep Learning,
Convolutional Neural Networks
BibRef
Lubana, E.S.,
Dick, R.P.,
Aggarwal, V.,
Pradhan, P.M.,
Minimalistic Image Signal Processing for Deep Learning Applications,
ICIP19(4165-4169)
IEEE DOI
1910
Deep learning accelerators, Image signal processor, RAW images, Covariate shift
BibRef
Saha, A.[Avinab],
Ram, K.S.[K. Sai],
Mukhopadhyay, J.[Jayanta],
Das, P.P.[Partha Pratim],
Patra, A.[Amit],
Fitness Based Layer Rank Selection Algorithm for Accelerating CNNs by
Candecomp/Parafac (CP) Decompositions,
ICIP19(3402-3406)
IEEE DOI
1910
CP Decompositions, FLRS, Accelerating CNNs, Rank Selection, Compression
BibRef
Xu, D.,
Lee, M.L.,
Hsu, W.,
Patch-Level Regularizer for Convolutional Neural Network,
ICIP19(3232-3236)
IEEE DOI
1910
BibRef
Kim, M.,
Park, C.,
Kim, S.,
Hong, T.,
Ro, W.W.,
Efficient Dilated-Winograd Convolutional Neural Networks,
ICIP19(2711-2715)
IEEE DOI
1910
Image processing and dilated convolution,
Winograd convolution, neural network, graphics processing unit
BibRef
Saporta, A.,
Chen, Y.,
Blot, M.,
Cord, M.,
Reve: Regularizing Deep Learning with Variational Entropy Bound,
ICIP19(1610-1614)
IEEE DOI
1910
Deep learning, regularization, invariance, information theory,
image understanding
BibRef
Zhao, W.,
Yi, R.,
Liu, Y.,
An Adaptive Filter for Deep Learning Networks on Large-Scale Point
Cloud,
ICIP19(1620-1624)
IEEE DOI
1910
Large-scale point cloud filtering, super-points, deep learning
BibRef
Choi, Y.,
Choi, J.,
Moon, H.,
Lee, J.,
Chang, J.,
Accelerating Framework for Simultaneous Optimization of Model
Architectures and Training Hyperparameters,
ICIP19(3831-3835)
IEEE DOI
1910
Deep Learning, Model Hyperparameters
BibRef
Zhe, W.,
Lin, J.,
Chandrasekhar, V.,
Girod, B.,
Optimizing the Bit Allocation for Compression of Weights and
Activations of Deep Neural Networks,
ICIP19(3826-3830)
IEEE DOI
1910
Deep Learning, Coding, Compression
BibRef
Lei, X.,
Liu, L.,
Zhou, Z.,
Sun, H.,
Zheng, N.,
Exploring Hardware Friendly Bottleneck Architecture in CNN for
Embedded Computing Systems,
ICIP19(4180-4184)
IEEE DOI
1910
Lightweight/Mobile CNN model, Model optimization,
Embedded System, Hardware Accelerating.
BibRef
Geng, X.[Xue],
Lin, J.[Jie],
Zhao, B.[Bin],
Kong, A.[Anmin],
Aly, M.M.S.[Mohamed M. Sabry],
Chandrasekhar, V.[Vijay],
Hardware-Aware Softmax Approximation for Deep Neural Networks,
ACCV18(IV:107-122).
Springer DOI
1906
BibRef
Groh, F.[Fabian],
Wieschollek, P.[Patrick],
Lensch, H.P.A.[Hendrik P. A.],
Flex-Convolution,
ACCV18(I:105-122).
Springer DOI
1906
BibRef
Yang, L.[Lu],
Song, Q.[Qing],
Li, Z.X.[Zuo-Xin],
Wu, Y.Q.[Ying-Qi],
Li, X.J.[Xiao-Jie],
Hu, M.J.[Meng-Jie],
Cross Connected Network for Efficient Image Recognition,
ACCV18(I:56-71).
Springer DOI
1906
BibRef
Ignatov, A.[Andrey],
Timofte, R.[Radu],
Chou, W.[William],
Wang, K.[Ke],
Wu, M.[Max],
Hartley, T.[Tim],
Van Gool, L.J.[Luc J.],
AI Benchmark: Running Deep Neural Networks on Android Smartphones,
PerceptualRest18(V:288-314).
Springer DOI
1905
BibRef
Li, X.,
Zhang, S.,
Jiang, B.,
Qi, Y.,
Chuah, M.C.,
Bi, N.,
DAC: Data-Free Automatic Acceleration of Convolutional Networks,
WACV19(1598-1606)
IEEE DOI
1904
convolutional neural nets, image classification,
Internet of Things, learning (artificial intelligence),
Deep learning
BibRef
He, Y.,
Liu, X.,
Zhong, H.,
Ma, Y.,
AddressNet: Shift-Based Primitives for Efficient Convolutional Neural
Networks,
WACV19(1213-1222)
IEEE DOI
1904
convolutional neural nets, coprocessors,
learning (artificial intelligence), parallel algorithms,
Fuses
BibRef
He, Z.Z.[Zhe-Zhi],
Gong, B.Q.[Bo-Qing],
Fan, D.L.[De-Liang],
Optimize Deep Convolutional Neural Network with Ternarized Weights
and High Accuracy,
WACV19(913-921)
IEEE DOI
1904
reduce to -1, 0, +1.
convolutional neural nets, embedded systems,
image classification, image coding, image representation,
Hardware
BibRef
Bicici, U.C.[Ufuk Can],
Keskin, C.[Cem],
Akarun, L.[Lale],
Conditional Information Gain Networks,
ICPR18(1390-1395)
IEEE DOI
1812
Decision trees, Neural networks, Computational modeling, Training,
Routing, Vegetation, Probability distribution
BibRef
Aldana, R.[Rodrigo],
Campos-Macías, L.[Leobardo],
Zamora, J.[Julio],
Gomez-Gutierrez, D.[David],
Cruz, A.[Adan],
Dynamic Learning Rate for Neural Networks:
A Fixed-Time Stability Approach,
ICPR18(1378-1383)
IEEE DOI
1812
Training, Artificial neural networks, Approximation algorithms,
Optimization, Pattern recognition, Heuristic algorithms, Lyapunov methods
BibRef
Kung, H.T.,
McDanel, B.,
Zhang, S.Q.,
Adaptive Tiling: Applying Fixed-size Systolic Arrays To Sparse
Convolutional Neural Networks,
ICPR18(1006-1011)
IEEE DOI
1812
Sparse matrices, Arrays, Convolution, Adaptive arrays,
Microprocessors, Adaptation models
BibRef
Grelsson, B.,
Felsberg, M.,
Improved Learning in Convolutional Neural Networks with Shifted
Exponential Linear Units (ShELUs),
ICPR18(517-522)
IEEE DOI
1812
convolution, feedforward neural nets, learning (artificial intelligence).
BibRef
Zheng, W.,
Zhang, Z.,
Accelerating the Classification of Very Deep Convolutional Network by
A Cascading Approach,
ICPR18(355-360)
IEEE DOI
1812
computational complexity, convolution, entropy,
feedforward neural nets, image classification,
Measurement uncertainty
BibRef
Zhong, G.,
Yao, H.,
Zhou, H.,
Merging Neurons for Structure Compression of Deep Networks,
ICPR18(1462-1467)
IEEE DOI
1812
Neurons, Neural networks, Merging, Computer architecture,
Matrix decomposition, Mathematical model, Prototypes
BibRef
Bhowmik, P.[Pankaj],
Pantho, M.J.H.[M. Jubaer Hossain],
Asadinia, M.[Marjan],
Bobda, C.[Christophe],
Design of a Reconfigurable 3D Pixel-Parallel Neuromorphic
Architecture for Smart Image Sensor,
ECVW18(786-7868)
IEEE DOI
1812
Computer architecture, Image sensors, Visualization,
Program processors, Clocks, Image processing
BibRef
Aggarwal, V.[Vaneet],
Wang, W.L.[Wen-Lin],
Eriksson, B.[Brian],
Sun, Y.F.[Yi-Fan],
Wan, W.Q.[Wen-Qi],
Wide Compression: Tensor Ring Nets,
CVPR18(9329-9338)
IEEE DOI
1812
Neural networks, Image coding, Shape, Merging, Computer architecture
BibRef
Ren, M.Y.[Meng-Ye],
Pokrovsky, A.[Andrei],
Yang, B.[Bin],
Urtasun, R.[Raquel],
SBNet: Sparse Blocks Network for Fast Inference,
CVPR18(8711-8720)
IEEE DOI
1812
Convolution, Kernel, Shape, Object detection, Task analysis
BibRef
Xie, G.T.[Guo-Tian],
Wang, J.D.[Jing-Dong],
Zhang, T.[Ting],
Lai, J.H.[Jian-Huang],
Hong, R.[Richang],
Qi, G.J.[Guo-Jun],
Interleaved Structured Sparse Convolutional Neural Networks,
CVPR18(8847-8856)
IEEE DOI
1812
Convolution, Kernel, Sparse matrices, Redundancy,
Computational modeling, Computer architecture, Computational complexity
BibRef
Kim, E.[Eunwoo],
Ahn, C.[Chanho],
Oh, S.H.[Song-Hwai],
NestedNet: Learning Nested Sparse Structures in Deep Neural Networks,
CVPR18(8669-8678)
IEEE DOI
1812
Task analysis, Knowledge engineering, Neural networks,
Computer architecture, Optimization, Redundancy
BibRef
Bulò, S.R.[Samuel Rota],
Porzi, L.[Lorenzo],
Kontschieder, P.[Peter],
In-place Activated BatchNorm for Memory-Optimized Training of DNNs,
CVPR18(5639-5647)
IEEE DOI
1812
Reduce memory needs.
Training, Buffer storage, Checkpointing, Memory management,
Standards, Semantics
BibRef
Zhang, D.,
clcNet: Improving the Efficiency of Convolutional Neural Network
Using Channel Local Convolutions,
CVPR18(7912-7919)
IEEE DOI
1812
Kernel, Computational modeling, Computational efficiency,
Convolutional neural networks, Stacking
BibRef
Kuen, J.,
Kong, X.,
Lin, Z.,
Wang, G.,
Yin, J.,
See, S.,
Tan, Y.,
Stochastic Downsampling for Cost-Adjustable Inference and Improved
Regularization in Convolutional Networks,
CVPR18(7929-7938)
IEEE DOI
1812
Training, Computational modeling, Computational efficiency,
Stochastic processes, Visualization, Network architecture
BibRef
Shazeer, N.,
Fatahalian, K.,
Mark, W.R.,
Mullapudi, R.T.,
HydraNets: Specialized Dynamic Architectures for Efficient Inference,
CVPR18(8080-8089)
IEEE DOI
1812
Computer architecture, Training, Computational modeling,
Task analysis, Computational efficiency, Optimization, Routing
BibRef
Rebuffi, S.,
Vedaldi, A.,
Bilen, H.,
Efficient Parametrization of Multi-domain Deep Neural Networks,
CVPR18(8119-8127)
IEEE DOI
1812
Task analysis, Neural networks, Adaptation models,
Feature extraction, Visualization, Computational modeling, Standards
BibRef
Cao, S.[Sen],
Liu, Y.Z.[Ya-Zhou],
Zhou, C.X.[Chang-Xin],
Sun, Q.S.[Quan-Sen],
Pongsak, L.S.[La-Sang],
Shen, S.M.[Sheng Mei],
ThinNet: An Efficient Convolutional Neural Network for Object
Detection,
ICPR18(836-841)
IEEE DOI
1812
Convolution, Computational modeling, Object detection,
Neural networks, Computer architecture, Training,
ThinNet
BibRef
Kobayashi, T.[Takumi],
t-vMF Similarity For Regularizing Intra-Class Feature Distribution,
CVPR21(6612-6621)
IEEE DOI
2111
WWW Link.
Code, Training. Training, Computational modeling, Focusing,
Pattern recognition, Noise measurement, Convolutional neural networks
BibRef
Kobayashi, T.[Takumi],
Analyzing Filters Toward Efficient ConvNet,
CVPR18(5619-5628)
IEEE DOI
1812
Convolution, Feature extraction, Neurons, Image reconstruction,
Visualization, Shape
BibRef
Chou, Y.,
Chan, Y.,
Lee, J.,
Chiu, C.,
Chen, C.,
Merging Deep Neural Networks for Mobile Devices,
EfficientDeep18(1767-17678)
IEEE DOI
1812
Task analysis, Convolution, Merging, Computational modeling,
Neural networks, Kernel, Computer architecture
BibRef
Ma, N.N.[Ning-Ning],
Zhang, X.Y.[Xiang-Yu],
Zheng, H.T.[Hai-Tao],
Sun, J.[Jian],
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture
Design,
ECCV18(XIV: 122-138).
Springer DOI
1810
BibRef
Zhang, X.Y.[Xiang-Yu],
Zhou, X.,
Lin, M.,
Sun, J.,
ShuffleNet: An Extremely Efficient Convolutional Neural Network for
Mobile Devices,
CVPR18(6848-6856)
IEEE DOI
1812
Convolution, Complexity theory, Computer architecture,
Mobile handsets, Computational modeling, Task analysis, Neural networks
BibRef
Prabhu, A.[Ameya],
Varma, G.[Girish],
Namboodiri, A.[Anoop],
Deep Expander Networks: Efficient Deep Networks from Graph Theory,
ECCV18(XIII: 20-36).
Springer DOI
1810
BibRef
Freeman, I.,
Roese-Koerner, L.,
Kummert, A.,
Effnet: An Efficient Structure for Convolutional Neural Networks,
ICIP18(6-10)
IEEE DOI
1809
Convolution, Computational modeling, Optimization, Hardware, Kernel,
Data compression, Convolutional neural networks,
real-time inference
BibRef
Elordi, U.[Unai],
Unzueta, L.[Luis],
Arganda-Carreras, I.[Ignacio],
Otaegui, O.[Oihana],
How Can Deep Neural Networks Be Generated Efficiently for Devices with
Limited Resources?,
AMDO18(24-33).
Springer DOI
1807
BibRef
Lee, T.K.[Tae Kwan],
Baddar, W.J.[Wissam J.],
Kim, S.T.[Seong Tae],
Ro, Y.M.[Yong Man],
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN,
MMMod18(I:117-129).
Springer DOI
1802
filter grouping in convolution layers.
BibRef
Véniat, T.[Tom],
Denoyer, L.[Ludovic],
Learning Time/Memory-Efficient Deep Architectures with Budgeted Super
Networks,
CVPR18(3492-3500)
IEEE DOI
1812
Computational modeling, Computer architecture,
Stochastic processes, Neural networks, Fabrics, Predictive models
BibRef
Huang, G.[Gao],
Liu, Z.[Zhuang],
van der Maaten, L.[Laurens],
Weinberger, K.Q.[Kilian Q.],
Densely Connected Convolutional Networks,
CVPR17(2261-2269)
IEEE DOI
1711
Award, CVPR. Convolution, Convolutional codes, Network architecture,
Neural networks, Road transportation, Training
BibRef
Huang, G.[Gao],
Sun, Y.[Yu],
Liu, Z.[Zhuang],
Sedra, D.[Daniel],
Weinberger, K.Q.[Kilian Q.],
Deep Networks with Stochastic Depth,
ECCV16(IV: 646-661).
Springer DOI
1611
BibRef
Huang, G.[Gao],
Liu, S.C.[Shi-Chen],
van der Maaten, L.[Laurens],
Weinberger, K.Q.[Kilian Q.],
CondenseNet: An Efficient DenseNet Using Learned Group Convolutions,
CVPR18(2752-2761)
IEEE DOI
1812
CNN on a phone.
Training, Computer architecture, Computational modeling, Standards,
Mobile handsets, Network architecture, Indexes
BibRef
Zhao, G.,
Zhang, Z.,
Guan, H.,
Tang, P.,
Wang, J.,
Rethinking ReLU to Train Better CNNs,
ICPR18(603-608)
IEEE DOI
1812
Convolution, Tensile stress, Network architecture,
Computational efficiency, Computational modeling, Pattern recognition
BibRef
Chan, M.,
Scarafoni, D.,
Duarte, R.,
Thornton, J.,
Skelly, L.,
Learning Network Architectures of Deep CNNs Under Resource
Constraints,
EfficientDeep18(1784-17847)
IEEE DOI
1812
Computer architecture, Computational modeling, Optimization,
Adaptation models, Network architecture, Linear programming, Training
BibRef
Bhagoji, A.N.[Arjun Nitin],
He, W.[Warren],
Li, B.[Bo],
Song, D.[Dawn],
Practical Black-Box Attacks on Deep Neural Networks Using Efficient
Query Mechanisms,
ECCV18(XII: 158-174).
Springer DOI
1810
BibRef
Kuen, J.[Jason],
Kong, X.F.[Xiang-Fei],
Wang, G.[Gang],
Tan, Y.P.[Yap-Peng],
DelugeNets: Deep Networks with Efficient and Flexible Cross-Layer
Information Inflows,
CEFR-LCV17(958-966)
IEEE DOI
1802
Complexity theory, Computational modeling,
Convolution, Correlation, Neural networks
BibRef
Singh, A.,
Kingsbury, N.G.,
Efficient Convolutional Network Learning Using Parametric Log Based
Dual-Tree Wavelet ScatterNet,
CEFR-LCV17(1140-1147)
IEEE DOI
1802
Computer architecture, Feature extraction,
Personal area networks, Standards, Training
BibRef
Liu, Z.,
Li, J.,
Shen, Z.,
Huang, G.,
Yan, S.,
Zhang, C.,
Learning Efficient Convolutional Networks through Network Slimming,
ICCV17(2755-2763)
IEEE DOI
1802
convolution, image classification,
learning (artificial intelligence), neural nets, CNNs,
Training
BibRef
Ioannou, Y.,
Robertson, D.,
Cipolla, R.,
Criminisi, A.,
Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups,
CVPR17(5977-5986)
IEEE DOI
1711
Computational complexity, Computational modeling,
Computer architecture, Convolution, Graphics processing units,
Neural networks, Training
BibRef
Lin, J.H.,
Xing, T.,
Zhao, R.,
Zhang, Z.,
Srivastava, M.,
Tu, Z.,
Gupta, R.K.,
Binarized Convolutional Neural Networks with Separable Filters for
Efficient Hardware Acceleration,
ECVW17(344-352)
IEEE DOI
1709
Backpropagation, Convolution, Field programmable gate arrays,
Filtering theory, Hardware, Kernel, Training
BibRef
Zhang, X.,
Li, Z.,
Loy, C.C.,
Lin, D.,
PolyNet: A Pursuit of Structural Diversity in Very Deep Networks,
CVPR17(3900-3908)
IEEE DOI
1711
Agriculture, Benchmark testing, Computational efficiency,
Diversity reception, Network architecture, Systematics, Training
BibRef
Yan, S.,
Keynotes: Deep learning for visual understanding:
Effectiveness vs. efficiency,
VCIP16(1-1)
IEEE DOI
1701
BibRef
Karmakar, P.,
Teng, S.W.,
Zhang, D.,
Liu, Y.,
Lu, G.,
Improved Tamura Features for Image Classification Using Kernel Based
Descriptors,
DICTA17(1-7)
IEEE DOI
1804
BibRef
And:
Improved Kernel Descriptors for Effective and Efficient Image
Classification,
DICTA17(1-8)
IEEE DOI
1804
BibRef
Earlier:
Combining Pyramid Match Kernel and Spatial Pyramid for Image
Classification,
DICTA16(1-8)
IEEE DOI
1701
Gabor filters,
image colour analysis, image segmentation.
feature extraction, image classification, image colour analysis,
image representation, effective image classification,
BibRef
Karmakar, P.,
Teng, S.W.,
Lu, G.,
Zhang, D.,
Rotation Invariant Spatial Pyramid Matching for Image Classification,
DICTA15(1-8)
IEEE DOI
1603
image classification
BibRef
Opitz, M.[Michael],
Possegger, H.[Horst],
Bischof, H.[Horst],
Efficient Model Averaging for Deep Neural Networks,
ACCV16(II: 205-220).
Springer DOI
1704
BibRef
Zhang, Z.M.[Zi-Ming],
Chen, Y.T.[Yu-Ting],
Saligrama, V.[Venkatesh],
Efficient Training of Very Deep Neural Networks for Supervised
Hashing,
CVPR16(1487-1495)
IEEE DOI
1612
BibRef
Smith, L.N.,
Cyclical Learning Rates for Training Neural Networks,
WACV17(464-472)
IEEE DOI
1609
Computational efficiency, Computer architecture, Neural networks,
Schedules, Training, Tuning
BibRef
Cardona-Escobar, A.F.[Andrés F.],
Giraldo-Forero, A.F.[Andrés F.],
Castro-Ospina, A.E.[Andrés E.],
Jaramillo-Garzón, J.A.[Jorge A.],
Efficient Hyperparameter Optimization in Convolutional Neural Networks
by Learning Curves Prediction,
CIARP17(143-151).
Springer DOI
1802
BibRef
Smith, L.N.,
Hand, E.M.,
Doster, T.,
Gradual DropIn of Layers to Train Very Deep Neural Networks,
CVPR16(4763-4771)
IEEE DOI
1612
BibRef
Pasquet, J.,
Chaumont, M.,
Subsol, G.,
Derras, M.,
Speeding-up a convolutional neural network by connecting an SVM
network,
ICIP16(2286-2290)
IEEE DOI
1610
Computational efficiency
BibRef
Park, W.S.,
Kim, M.,
CNN-based in-loop filtering for coding efficiency improvement,
IVMSP16(1-5)
IEEE DOI
1608
Convolution
BibRef
Moons, B.[Bert],
de Brabandere, B.[Bert],
Van Gool, L.J.[Luc J.],
Verhelst, M.[Marian],
Energy-efficient ConvNets through approximate computing,
WACV16(1-8)
IEEE DOI
1606
Approximation algorithms
BibRef
Li, N.,
Takaki, S.,
Tomiokay, Y.,
Kitazawa, H.,
A multistage dataflow implementation of a Deep Convolutional Neural
Network based on FPGA for high-speed object recognition,
Southwest16(165-168)
IEEE DOI
1605
Acceleration
BibRef
Hsu, F.C.,
Gubbi, J.,
Palaniswami, M.,
Learning Efficiently- The Deep CNNs-Tree Network,
DICTA15(1-7)
IEEE DOI
1603
learning (artificial intelligence)
BibRef
Highlander, T.[Tyler],
Rodriguez, A.[Andres],
Very Efficient Training of Convolutional Neural Networks using Fast
Fourier Transform and Overlap-and-Add,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Zou, X.Y.[Xiao-Yi],
Xu, X.M.[Xiang-Min],
Qing, C.M.[Chun-Mei],
Xing, X.F.[Xiao-Fen],
High speed deep networks based on Discrete Cosine Transformation,
ICIP14(5921-5925)
IEEE DOI
1502
Accuracy
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Net Pruning .