14.5.10.8.7 Efficient Implementations Convolutional Neural Networks

Chapter Contents (Back)
CNN. Efficient Implementation. Efficiency issues, low power, etc.
See also Neural Net Pruning.
See also Neural Net Quantization.

Zhang, X.Y.[Xiang-Yu], Zou, J.H.[Jian-Hua], He, K.M.[Kai-Ming], Sun, J.[Jian],
Accelerating Very Deep Convolutional Networks for Classification and Detection,
PAMI(38), No. 10, October 2016, pp. 1943-1955.
IEEE DOI 1609
Acceleration BibRef

He, Y., Zhang, X.Y.[Xiang-Yu], Sun, J.[Jian],
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 BibRef

Zhang, X.Y.[Xiang-Yu], Zou, J.H.[Jian-Hua], Ming, X.[Xiang], He, K.M.[Kai-Ming], Sun, J.[Jian],
Efficient and accurate approximations of nonlinear convolutional networks,
CVPR15(1984-1992)
IEEE DOI 1510
BibRef

He, K.M.[Kai-Ming], Zhang, X.Y.[Xiang-Yu], Ren, S.Q.[Shao-Qing], Sun, J.[Jian],
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification,
ICCV15(1026-1034)
IEEE DOI 1602
Adaptation models BibRef

Sze, V., Chen, Y.H., Yang, T.J., Emer, J.S.,
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, spatial architectures BibRef

Cavigelli, L., Benini, L.,
Origami: A 803-GOp/s/W Convolutional Network Accelerator,
CirSysVideo(27), No. 11, November 2017, pp. 2461-2475.
IEEE DOI 1712
Feature extraction, Machine learning, Mobile communication, Neural networks, very large scale integration BibRef

Cavigelli, L., Benini, L.,
CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams,
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, object detection BibRef

Ghesu, F.C.[Florin C.], Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J.[Joachim], Comaniciu, D.,
Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing,
MedImg(35), No. 5, May 2016, pp. 1217-1228.
IEEE DOI 1605
Context BibRef

Revathi, A.R., Kumar, D.[Dhananjay],
An efficient system for anomaly detection using deep learning classifier,
SIViP(11), No. 2, February 2017, pp. 291-299.
WWW Link. 1702
BibRef

Sun, B., Feng, H.,
Efficient Compressed Sensing for Wireless Neural Recording: A Deep Learning Approach,
SPLetters(24), No. 6, June 2017, pp. 863-867.
IEEE DOI 1705
Compressed sensing, Cost function, Dictionaries, Sensors, Training, Wireless communication, Wireless sensor networks, Compressed sensing (CS), deep neural network, wireless neural recording BibRef

Xu, T.B.[Ting-Bing], Yang, P.P.[Pei-Pei], Zhang, X.Y.[Xu-Yao], Liu, C.L.[Cheng-Lin],
LightweightNet: Toward fast and lightweight convolutional neural networks via architecture distillation,
PR(88), 2019, pp. 272-284.
Elsevier DOI 1901
Deep network acceleration and compression, Architecture distillation, Lightweight network BibRef

Kim, D.H.[Dae Ha], Lee, M.K.[Min Kyu], Lee, S.H.[Seung Hyun], Song, B.C.[Byung Cheol],
Macro unit-based convolutional neural network for very light-weight deep learning,
IVC(87), 2019, pp. 68-75.
Elsevier DOI 1906
BibRef
Earlier: A1, A3, A4, Only:
MUNet: Macro Unit-Based Convolutional Neural Network for Mobile Devices,
EfficientDeep18(1749-17498)
IEEE DOI 1812
Deep neural networks, Light-weight deep learning, Macro-unit. Convolution, Computational complexity, Mobile handsets, Neural networks, Performance evaluation BibRef

Zhang, C.Y.[Chun-Yang], Zhao, Q.[Qi], Chen, C.L.P.[C.L. Philip], Liu, W.X.[Wen-Xi],
Deep compression of probabilistic graphical networks,
PR(96), 2019, pp. 106979.
Elsevier DOI 1909
Deep compression, Probabilistic graphical models, Probabilistic graphical networks, Deep learning BibRef

Brillet, L.F., Mancini, S., Cleyet-Merle, S., Nicolas, M.,
Tunable CNN Compression Through Dimensionality Reduction,
ICIP19(3851-3855)
IEEE DOI 1910
CNN, PCA, compression BibRef

Lin, S.H.[Shao-Hui], Ji, R.R.[Rong-Rong], Chen, C.[Chao], Tao, D.C.[Da-Cheng], Luo, J.B.[Jie-Bo],
Holistic CNN Compression via Low-Rank Decomposition with Knowledge Transfer,
PAMI(41), No. 12, December 2019, pp. 2889-2905.
IEEE DOI 1911
Knowledge transfer, Image coding, Task analysis, Information exchange, Computational modeling, CNN acceleration BibRef

Zhang, L.[Lin], Bu, X.K.[Xiao-Kang], Li, B.[Bing],
XNORCONV: CNNs accelerator implemented on FPGA using a hybrid CNNs structure and an inter-layer pipeline method,
IET-IPR(14), No. 1, January 2020, pp. 105-113.
DOI Link 1912
BibRef

Chen, Z., Fan, K., Wang, S., Duan, L., Lin, W., Kot, A.C.,
Toward Intelligent Sensing: Intermediate Deep Feature Compression,
IP(29), 2020, pp. 2230-2243.
IEEE DOI 2001
Visualization, Image coding, Task analysis, Feature extraction, Deep learning, Video coding, Standardization, Deep learning, feature compression BibRef

Lobel, H.[Hans], Vidal, R.[René], Soto, A.[Alvaro],
CompactNets: Compact Hierarchical Compositional Networks for Visual Recognition,
CVIU(191), 2020, pp. 102841.
Elsevier DOI 2002
Deep learning, Regularization, Group sparsity, Image categorization BibRef

Ding, L.[Lin], Tian, Y.H.[Yong-Hong], Fan, H.F.[Hong-Fei], Chen, C.H.[Chang-Huai], Huang, T.J.[Tie-Jun],
Joint Coding of Local and Global Deep Features in Videos for Visual Search,
IP(29), 2020, pp. 3734-3749.
IEEE DOI 2002
Local deep feature, joint coding, visual search, inter-feature correlation BibRef

Browne, D.[David], Giering, M.[Michael], Prestwich, S.[Steven],
PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Liu, Z.C.[Ze-Chun], Luo, W.H.[Wen-Han], Wu, B.Y.[Bao-Yuan], Yang, X.[Xin], Liu, W.[Wei], Cheng, K.T.[Kwang-Ting],
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 Representational Capability and Advanced Training Algorithm,
ECCV18(XV: 747-763).
Springer DOI 1810
BibRef

Sun, F.Z.[Feng-Zhen], Li, S.J.[Shao-Jie], Wang, S.H.[Shao-Hua], Liu, Q.J.[Qing-Jun], Zhou, L.X.[Li-Xin],
CostNet: A Concise Overpass Spatiotemporal Network for Predictive Learning,
IJGI(9), No. 4, 2020, pp. xx-yy.
DOI Link 2005
ResNet to deal with temporal. BibRef

Kalayeh, M.M.[Mahdi M.], Shah, M.[Mubarak],
Training Faster by Separating Modes of Variation in Batch-Normalized Models,
PAMI(42), No. 6, June 2020, pp. 1483-1500.
IEEE DOI 2005
Training, Kernel, Mathematical model, Transforms, Probability density function, Statistics, Acceleration, fisher vector BibRef

Ma, W.C.[Wen-Chi], Wu, Y.W.[Yuan-Wei], Cen, F.[Feng], Wang, G.H.[Guang-Hui],
MDFN: Multi-scale deep feature learning network for object detection,
PR(100), 2020, pp. 107149.
Elsevier DOI 2005
Deep feature learning, Multi-scale, Semantic and contextual information, Small and occluded objects BibRef

Patel, K.[Krushi], Wang, G.H.[Guang-Hui],
A discriminative channel diversification network for image classification,
PRL(153), 2022, pp. 176-182.
Elsevier DOI 2201
Image classification, Discriminative features, Channel attention mechanism BibRef

Ma, W.C.[Wen-Chi], Wu, Y.W.[Yuan-Wei], Wang, Z., Wang, G.H.[Guang-Hui],
MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection,
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, Merging, Task analysis, Visualization, Robustness BibRef

Ma, L.H.[Long-Hua], Fan, H.Y.[Hang-Yu], Lu, Z.M.[Zhe-Ming], Tian, D.[Dong],
Acceleration of multi-task cascaded convolutional networks,
IET-IPR(14), No. 11, September 2020, pp. 2435-2441.
DOI Link 2009
BibRef

Jiang, Y.G.[Yu-Gang], Cheng, C.M.[Chang-Mao], Lin, H.Y.[Hang-Yu], Fu, Y.W.[Yan-Wei],
Learning Layer-Skippable Inference Network,
IP(29), 2020, pp. 8747-8759.
IEEE DOI 2009
Task analysis, Visualization, Computational modeling, Biological information theory, Computational efficiency, Neurons, neural networks BibRef

Fang, Z.Y.[Zhen-Yu], Ren, J.C.[Jin-Chang], Marshall, S.[Stephen], Zhao, H.M.[Hui-Min], Wang, S.[Song], Li, X.L.[Xue-Long],
Topological optimization of the DenseNet with pretrained-weights inheritance and genetic channel selection,
PR(109), 2021, pp. 107608.
Elsevier DOI 2009
Deep convolutional neural networks, Genetic algorithms, Parameter reduction, Structure optimization, DenseNet BibRef

Li, G.Q.[Guo-Qing], Zhang, M.[Meng], Li, J.[Jiaojie], Lv, F.[Feng], Tong, G.D.[Guo-Dong],
Efficient densely connected convolutional neural networks,
PR(109), 2021, pp. 107610.
Elsevier DOI 2009
Convolutional neural networks, Classification, Parameter efficiency, Densely connected BibRef

Yang, Y.Q.[Yong-Quan], Lv, H.J.[Hai-Jun], Chen, N.[Ning], Wu, Y.[Yang], Zheng, J.Y.[Jia-Yi], Zheng, Z.X.[Zhong-Xi],
Local minima found in the subparameter space can be effective for ensembles of deep convolutional neural networks,
PR(109), 2021, pp. 107582.
Elsevier DOI 2009
Ensemble learning, Ensemble selection, Ensemble fusion, Deep convolutional neural network BibRef

Gürhanli, A.[Ahmet],
Accelerating convolutional neural network training using ProMoD backpropagation algorithm,
IET-IPR(14), No. 13, November 2020, pp. 2957-2964.
DOI Link 2012
BibRef

Xi, J.B.[Jiang-Bo], Ersoy, O.K.[Okan K.], Fang, J.W.[Jian-Wu], Cong, M.[Ming], Wu, T.J.[Tian-Jun], Zhao, C.Y.[Chao-Ying], Li, Z.H.[Zhen-Hong],
Wide Sliding Window and Subsampling Network for Hyperspectral Image Classification,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Xi, J.B.[Jiang-Bo], Cong, M.[Ming], Ersoy, O.K.[Okan K.], Zou, W.B.[Wei-Bao], Zhao, C.Y.[Chao-Ying], Li, Z.H.[Zhen-Hong], Gu, J.K.[Jun-Kai], Wu, T.J.[Tian-Jun],
Dynamic Wide and Deep Neural Network for Hyperspectral Image Classification,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Xi, J.B.[Jiang-Bo], Ersoy, O.K.[Okan K.], Cong, M.[Ming], Zhao, C.Y.[Chao-Ying], Qu, W.[Wei], Wu, T.J.[Tian-Jun],
Wide and Deep Fourier Neural Network for Hyperspectral Remote Sensing Image Classification,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

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 Object Detection and Aerial Tracking from UAV Images,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Cancela, B.[Brais], Bolón-Canedo, V.[Verónica], Alonso-Betanzos, A.[Amparo],
E2E-FS: An End-to-End Feature Selection Method for Neural Networks,
PAMI(45), No. 7, July 2023, pp. 8311-8323.
IEEE DOI 2306
Feature extraction, Training, Convergence, Memory management, Force, Computational modeling, Computational efficiency, non-convex problem BibRef

Cancela, B.[Brais], Bolón-Canedo, V.[Verónica], Alonso-Betanzos, A.[Amparo],
Can data placement be effective for Neural Networks classification tasks? Introducing the Orthogonal Loss,
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 BibRef

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], Plaza, J.[Javier], 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, 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.J.[Shi-Ji], 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

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

Mehta, S.[Sachin], Hajishirzi, H.[Hannaneh], Rastegari, M.[Mohammad],
DiCENet: Dimension-Wise Convolutions for Efficient Networks,
PAMI(44), No. 5, May 2022, pp. 2416-2425.
IEEE DOI 2204
Tensors, Standards, Kernel, Convolutional codes, Task analysis, Object detection, efficient networks BibRef

Mehta, S.[Sachin], Rastegari, M.[Mohammad], Shapiro, L.[Linda], Hajishirzi, H.[Hannaneh],
ESPNetv2: A Light-Weight, Power Efficient, and General Purpose Convolutional Neural Network,
CVPR19(9182-9192).
IEEE DOI 2002
BibRef

Han, K.[Kai], Wang, Y.H.[Yun-He], Xu, C.[Chang], Guo, J.Y.[Jian-Yuan], Xu, C.J.[Chun-Jing], Wu, E.[Enhua], Tian, Q.[Qi],
GhostNets on Heterogeneous Devices via Cheap Operations,
IJCV(130), No. 1, January 2022, pp. 1050-1069.
Springer DOI 2204
Code, Neural Networks.
WWW Link.
WWW Link. BibRef

Schonsheck, S.C.[Stefan C.], Dong, B.[Bin], Lai, R.J.[Rong-Jie],
Parallel Transport Convolution: Deformable Convolutional Networks on Manifold-Structured Data,
SIIMS(15), No. 1, 2022, pp. 367-386.
DOI Link 2204
Generalizing convolutions on three-dimensional surfaces. Aid in implementing wavelet and CNN on surfaces. BibRef

Han, K.[Kai], Wang, Y.H.[Yun-He], Xu, C.[Chang], Xu, C.J.[Chun-Jing], Wu, E.[Enhua], Tao, D.C.[Da-Cheng],
Learning Versatile Convolution Filters for Efficient Visual Recognition,
PAMI(44), No. 11, November 2022, pp. 7731-7746.
IEEE DOI 2210
Convolution, Neural networks, Quantization (signal), Computational modeling, Convolutional neural networks, versatile filters BibRef

Liu, C.[Chang], Zhang, X.S.[Xi-Shan], Zhang, R.[Rui], Li, L.[Ling], Zhou, S.Y.[Shi-Yi], Huang, D.[Di], Li, Z.[Zhen], Du, Z.D.[Zi-Dong], Liu, S.L.[Shao-Li], Chen, T.S.[Tian-Shi],
Rethinking the Importance of Quantization Bias, Toward Full Low-Bit Training,
IP(31), 2022, pp. 7006-7019.
IEEE DOI 2212
Quantize all levels of the network to reduce computational cost. Works better than expected. Quantization (signal), Training, Computer crashes, Convolutional neural networks, Machine translation, quantization BibRef

Yang, T.J.N.[Tao-Jian-Nan], Zhu, S.J.[Si-Jie], Mendieta, M.[Matias], Wang, P.[Pu], Balakrishnan, R.[Ravikumar], Lee, M.W.[Min-Woo], Han, T.[Tao], Shah, M.[Mubarak], Chen, C.[Chen],
MutualNet: Adaptive ConvNet via Mutual Learning From Different Model Configurations,
PAMI(45), No. 1, January 2023, pp. 811-827.
IEEE DOI 2212
Training, Adaptation models, Task analysis, Adaptive systems, Computational modeling, Complexity theory, Neural networks, deep learning BibRef

Yang, T.J.N.[Tao-Jian-Nan], 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. BibRef

Jahani-Nezhad, T.[Tayyebeh], Maddah-Ali, M.A.[Mohammad Ali],
Berrut Approximated Coded Computing: Straggler Resistance Beyond Polynomial Computing,
PAMI(45), No. 1, January 2023, pp. 111-122.
IEEE DOI 2212
Training with parallel systems. Interpolation, Servers, Codes, Computational modeling, Task analysis, Numerical models, Encoding, Distributed learning, coded computing BibRef

Wu, Y.M.[Yi-Ming], Li, R.X.[Rui-Xiang], Yu, Y.L.[Yun-Long], Li, X.[Xi],
Reparameterized attention for convolutional neural networks,
PRL(164), 2022, pp. 89-95.
Elsevier DOI 2212
Attention mechanism, Bayesian variational inference, Reparameterization, Uncertainty, Batch shaping BibRef

Xi, Y.[Yue], Jia, W.J.[Wen-Jing], Miao, Q.G.[Qi-Guang], Liu, X.Z.[Xiang-Zeng], Fan, X.C.[Xiao-Chen], Lou, J.[Jian],
DyCC-NEt: Dynamic Context Collection Network for Input-Aware Drone-View Object Detection,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
To deploy in lightweight vehicles. BibRef

Li, C.L.[Chang-Lin], Wang, G.R.[Guang-Run], Wang, B.[Bing], Liang, X.D.[Xiao-Dan], Li, Z.H.[Zhi-Hui], Chang, X.J.[Xiao-Jun],
DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Vision Transformers,
PAMI(45), No. 4, April 2023, pp. 4430-4446.
IEEE DOI 2303
Training, Logic gates, Routing, Transformers, Neural networks, Optimization, Adaptive inference, dynamic networks, vision transformer BibRef

Wang, L.G.[Long-Guang], Guo, Y.L.[Yu-Lan], Dong, X.Y.[Xiao-Yu], Wang, Y.Q.[Ying-Qian], Ying, X.[Xinyi], Lin, Z.P.[Zai-Ping], An, W.[Wei],
Exploring Fine-Grained Sparsity in Convolutional Neural Networks for Efficient Inference,
PAMI(45), No. 4, April 2023, pp. 4474-4493.
IEEE DOI 2303
Point cloud compression, Semantics, Biological neural networks, Task analysis, Neurons, Image segmentation, Costs, Neural network, stereo matching BibRef

Wang, S.[Siyu], Li, W.[WeiPeng], Lu, R.[Ruitao], Yang, X.G.[Xiao-Gang], Xi, J.X.[Jian-Xiang], Gao, J.[Jiuan],
Neural network acceleration methods via selective activation,
IET-CV(17), No. 3, 2023, pp. 295-308.
DOI Link 2305
convolutional neural nets, image processing, neural net architecture BibRef

Xiao, P.H.[Peng-Hao], Xu, T.[Teng], Xiao, X.Y.[Xia-Yang], Li, W.S.[Wei-Song], Wang, H.P.[Hai-Peng],
Distillation Sparsity Training Algorithm for Accelerating Convolutional Neural Networks in Embedded Systems,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
BibRef

Haider, U.[Usman], Hanif, M.[Muhammad], Rashid, A.[Ahmar], Hussain, S.F.[Syed Fawad],
Dictionary-enabled efficient training of ConvNets for image classification,
IVC(135), 2023, pp. 104718.
Elsevier DOI 2306
Sparse representation, Convolution neural networks, Deep learning, Dictionary learning, Image classification BibRef

Huang, L.[Lei], Qin, J.[Jie], Zhou, Y.[Yi], Zhu, F.[Fan], Liu, L.[Li], Shao, L.[Ling],
Normalization Techniques in Training DNNs: Methodology, Analysis and Application,
PAMI(45), No. 8, August 2023, pp. 10173-10196.
IEEE DOI 2307
Training, Optimization, Covariance matrices, Task analysis, Tensors, Decorrelation, Biological neural networks, Batch normalization, weight normalization BibRef

Zhang, H.[Hu], Zu, K.[Keke], Lu, J.[Jian], Zou, Y.[Yuru], Meng, D.Y.[De-Yu],
Epsanet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network,
ACCV22(III:541-557).
Springer DOI 2307
BibRef

Dong, M.J.[Min-Jing], Chen, X.H.[Xing-Hao], Wang, Y.H.[Yun-He], Xu, C.[Chang],
Improving Lightweight AdderNet via Distillation from L2 to L1-norm,
IP(32), 2023, pp. 5524-5536.
IEEE DOI 2310
Adder Neural Networks (ANNs) are proposed to replace expensive multiplication operations in Convolutional Neural Networks (CNNs) with cheap additions. BibRef

Zhang, H.[Hao], Lai, S.Q.[Shen-Qi], Wang, Y.X.[Ya-Xiong], Da, Z.Y.[Zong-Yang], Dun, Y.J.[Yu-Jie], Qian, X.M.[Xue-Ming],
SCGNet: Shifting and Cascaded Group Network,
CirSysVideo(33), No. 9, September 2023, pp. 4997-5008.
IEEE DOI 2310
BibRef

Sepehri, Y.M.[Ya-Min], Pad, P.[Pedram], Kündig, C.[Clément], Frossard, P.[Pascal], Dunbar, L.A.[L. Andrea],
Privacy-Preserving Image Acquisition for Neural Vision Systems,
MultMed(25), 2023, pp. 6232-6244.
IEEE DOI 2311
BibRef

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 BibRef

Liu, M.[Min], Zhou, C.C.[Chang-Chun], Qiu, S.Y.[Si-Yuan], He, Y.F.[Yi-Fan], Jiao, H.L.[Hai-Long],
CNN Accelerator at the Edge With Adaptive Zero Skipping and Sparsity-Driven Data Flow,
CirSysVideo(33), No. 12, December 2023, pp. 7084-7095.
IEEE DOI 2312
BibRef

Lin, S.H.[Shao-Hui], Ji, B.[Bo], Ji, R.R.[Rong-Rong], Yao, A.[Angela],
A closer look at branch classifiers of multi-exit architectures,
CVIU(239), 2024, pp. 103900.
Elsevier DOI 2402
Multi-exit architectures, Knowledge consistency, Branch classifiers, Model compression and acceleration BibRef


Haque, M.[Mirazul], Chen, S.[Simin], Haque, W.[Wasif], Liu, C.[Cong], Yang, W.[Wei],
AntiNODE: Evaluating Efficiency Robustness of Neural ODEs,
REDLCV23(1499-1509)
IEEE DOI 2401
BibRef

Han, Y.Z.[Yi-Zeng], Han, D.C.[Dong-Chen], Liu, Z.[Zeyu], Wang, Y.L.[Yu-Lin], Pan, X.[Xuran], Pu, Y.F.[Yi-Fan], Deng, C.[Chao], Feng, J.[Junlan], Song, S.[Shiji], Huang, G.[Gao],
Dynamic Perceiver for Efficient Visual Recognition,
ICCV23(5969-5979)
IEEE DOI Code:
WWW Link. 2401
BibRef

Huang, Z.P.[Zhi-Peng], Zhang, Z.Z.[Zhi-Zheng], Lan, C.L.[Cui-Ling], Zha, Z.J.[Zheng-Jun], Lu, Y.[Yan], Guo, B.[Baining],
Adaptive Frequency Filters As Efficient Global Token Mixers,
ICCV23(6026-6036)
IEEE DOI Code:
WWW Link. 2401
BibRef

Park, S.[Song], Chun, S.[Sanghyuk], Heo, B.[Byeongho], Kim, W.[Wonjae], Yun, S.[Sangdoo],
SeiT: Storage-Efficient Vision Training with Tokens Using 1% of Pixel Storage,
ICCV23(17202-17213)
IEEE DOI Code:
WWW Link. 2401
BibRef

Ancilotto, A.[Alberto], Paissan, F.[Francesco], Farella, E.[Elisabetta],
XiNet: Efficient Neural Networks for tinyML,
ICCV23(16922-16931)
IEEE DOI 2401
BibRef

Zhang, J.N.[Jiang-Ning], Li, X.T.[Xiang-Tai], Li, J.[Jian], Liu, L.[Liang], Xue, Z.[Zhucun], Zhang, B.[Boshen], Jiang, Z.K.[Zheng-Kai], Huang, T.X.[Tian-Xin], Wang, Y.[Yabiao], Wang, C.J.[Cheng-Jie],
Rethinking Mobile Block for Efficient Attention-based Models,
ICCV23(1389-1400)
IEEE DOI 2401
BibRef

Lazzaro, D.[Dario], Cinŕ, A.E.[Antonio Emanuele], Pintor, M.[Maura], Demontis, A.[Ambra], Biggio, B.[Battista], Roli, F.[Fabio], Pelillo, M.[Marcello],
Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training,
CIAP23(II:515-526).
Springer DOI 2312
BibRef

Polina, K.[Karpikova], Ekaterina, R.[Radionova], Anastasia, Y.[Yaschenko], Andrei, S.[Spiridonov], Leonid, K.[Kostyushko], Riccardo, F.[Fabbricatore], Aleksei, I.[Ivakhnenko],
FIANCEE: Faster Inference of Adversarial Networks via Conditional Early Exits,
CVPR23(12032-12043)
IEEE DOI 2309
BibRef

Li, J.F.[Jia-Feng], Wen, Y.[Ying], He, L.H.[Liang-Hua],
SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy,
CVPR23(6153-6162)
IEEE DOI 2309
BibRef

Endo, T.[Takeshi], Kaji, S.[Seigo], Matono, H.[Haruki], Takemura, M.[Masayuki], Shima, T.[Takeshi],
Re-Parameterization Making GC-Net-Style 3dconvnets More Efficient,
ACCV22(I:311-325).
Springer DOI 2307
BibRef

Bertrand, T.[Théo], Makaroff, N.[Nicolas], Cohen, L.D.[Laurent D.],
Fast Marching Energy CNN,
SSVM23(276-287).
Springer DOI 2307
BibRef

Cannella, C.[Chris], Tarokh, V.[Vahid],
Semi-Empirical Objective Functions for MCMC Proposal Optimization,
ICPR22(4758-4764)
IEEE DOI 2212
Weight measurement, Training, Linear programming, Behavioral sciences, Proposals, Optimization BibRef

Dai, L.J.[Ling-Jun], Zhang, Q.T.[Qing-Tian], Wu, H.Q.[Hua-Qiang],
Improving the accuracy of neural networks in analog computing-in-memory systems by analog weight,
ICPR22(2971-2978)
IEEE DOI 2212
Degradation, Weight measurement, Performance evaluation, Quantization (signal), Neural networks, Programming, Energy efficiency BibRef

Zhou, H.[Han], Ashrafi, A.[Aida], Blaschko, M.B.[Matthew B.],
Combinatorial optimization for low bit-width neural networks,
ICPR22(2246-2252)
IEEE DOI 2212
Training, Neural networks, Nonhomogeneous media, Hardware, Data models, Risk management BibRef

Trusov, A.[Anton], Limonova, E.[Elena], Nikolaev, D.[Dmitry], Arlazarov, V.V.[Vladimir V.],
Fast matrix multiplication for binary and ternary CNNs on ARM CPU,
ICPR22(3176-3182)
IEEE DOI 2212
Neon, Neural networks, Memory management, Inference algorithms, Mobile handsets, Libraries, Computational efficiency BibRef

Ignatov, A.[Andrey], Malivenko, G.[Grigory], Timofte, R.[Radu], Tseng, Y.[Yu], Xu, Y.S.[Yu-Syuan], Yu, P.H.[Po-Hsiang], Chiang, C.M.[Cheng-Ming], Kuo, H.K.[Hsien-Kai], Chen, M.H.[Min-Hung], Cheng, C.M.[Chia-Ming], Van Gool, L.J.[Luc J.],
PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks,
ICPR22(677-684)
IEEE DOI 2212
Performance evaluation, Visualization, Pipelines, Cameras, Mobile handsets, Software BibRef

Liang, Z.W.[Zhi-Wei], Zhou, Y.[Yuezhi],
Dispense Mode for Inference to Accelerate Branchynet,
ICIP22(1246-1250)
IEEE DOI 2211
Improve time by dropping samples, but has quality trade-off. Deep learning, Computational modeling, Neural networks, Throughput, Internet of Things, deep neural networks, computer vision, model optimization BibRef

Duggal, R.[Rahul], Zhou, H.[Hao], Yang, S.[Shuo], Fang, J.[Jun], Xiong, Y.J.[Yuan-Jun], Xia, W.[Wei],
Towards Regression-Free Neural Networks for Diverse Compute Platforms,
ECCV22(XXXVII:598-614).
Springer DOI 2211
BibRef

Yong, H.W.[Hong-Wei], Zhang, L.[Lei],
An Embedded Feature Whitening Approach to Deep Neural Network Optimization,
ECCV22(XXIII:334-351).
Springer DOI 2211
BibRef

Kwan, H.M.[Ho Man], Song, S.[Shenghui],
SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling,
ECCV22(XXI:229-244).
Springer DOI 2211
BibRef

Upadhyay, U.[Uddeshya], Karthik, S.[Shyamgopal], Chen, Y.B.[Yan-Bei], Mancini, M.[Massimiliano], Akata, Z.[Zeynep],
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks,
ECCV22(XII:299-317).
Springer DOI 2211
BibRef

Lu, Y.[Yao], Yang, W.[Wen], Zhang, Y.Z.[Yun-Zhe], Chen, Z.H.[Zuo-Hui], Chen, J.Y.[Jin-Yin], Xuan, Q.[Qi], Wang, Z.[Zhen], Yang, X.[Xiaoniu],
Understanding the Dynamics of DNNs Using Graph Modularity,
ECCV22(XII:225-242).
Springer DOI 2211
BibRef

Molchanov, P.[Pavlo], Hall, J.[Jimmy], Yin, H.X.[Hong-Xu], Kautz, J.[Jan], Fusi, N.[Nicolo], Vahdat, A.[Arash],
LANA: Latency Aware Network Acceleration,
ECCV22(XII:137-156).
Springer DOI 2211
BibRef

Han, Y.Z.[Yi-Zeng], Pu, Y.F.[Yi-Fan], Lai, Z.H.[Zi-Hang], Wang, C.F.[Chao-Fei], Song, S.J.[Shi-Ji], Cao, J.F.[Jun-Feng], Huang, W.H.[Wen-Hui], Deng, C.[Chao], Huang, G.[Gao],
Learning to Weight Samples for Dynamic Early-Exiting Networks,
ECCV22(XI:362-378).
Springer DOI 2211
BibRef

Hu, Q.H.[Qing-Hao], Li, G.[Gang], Wu, Q.[Qiman], Cheng, J.[Jian],
PalQuant: Accelerating High-Precision Networks on Low-Precision Accelerators,
ECCV22(XI:312-327).
Springer DOI 2211
BibRef

Yang, X.C.[Xue-Can], Chaudhuri, S.[Sumanta], Likforman, L.[Laurence], Naviner, L.[Lirida],
Minconvnets: a New Class of Multiplication-Less Neural Networks,
ICIP22(881-885)
IEEE DOI 2211
Training, Correlation, Quantization (signal), Transfer learning, Neural networks, Hardware BibRef

Shipard, J.[Jordan], Wiliem, A.[Arnold], Fookes, C.[Clinton],
Does Interference Exist When Training a Once-For-All Network?,
EVW22(3618-3627)
IEEE DOI 2210

WWW Link. Training, Codes, Sociology, Neural networks, Interference BibRef

Zhang, H.[Hang], Wu, C.[Chongruo], Zhang, Z.Y.[Zhong-Yue], Zhu, Y.[Yi], Lin, H.B.[Hai-Bin], Zhang, Z.[Zhi], Sun, Y.[Yue], He, T.[Tong], Mueller, J.[Jonas], Manmatha, R., Li, M.[Mu], Smola, A.[Alexander],
ResNeSt: Split-Attention Networks,
ECV22(2735-2745)
IEEE DOI 2210
Deep learning, Computational modeling, Pattern recognition, Convolutional neural networks BibRef

Chen, J.[Jierun], He, T.L.[Tian-Lang], Zhuo, W.P.[Wei-Peng], Ma, L.[Li], Ha, S.[Sangtae], Chan, S.H.G.[S.H. Gary],
TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing,
CVPR22(12538-12548)
IEEE DOI 2210

WWW Link. Training, Visualization, Convolution, Face recognition, Microscopy, Network architecture, Vision applications and systems BibRef

Hu, M.[Mu], Feng, J.[Junyi], Hua, J.[Jiashen], Lai, B.[Baisheng], Huang, J.Q.[Jian-Qiang], Gong, X.J.[Xiao-Jin], Hua, X.[Xiansheng],
Online Convolutional Reparameterization,
CVPR22(558-567)
IEEE DOI 2210
Training, Convolutional codes, Costs, Convolution, Biological system modeling, Computational modeling, Efficient learning and inferences BibRef

Ding, X.H.[Xiao-Han], Chen, H.H.[Hong-Hao], Zhang, X.Y.[Xiang-Yu], Han, J.G.[Jun-Gong], Ding, G.[Guiguang],
RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality,
CVPR22(568-577)
IEEE DOI 2210
Convolutional codes, Image recognition, Computational modeling, Semantics, Merging, Feature extraction, Deep learning architectures and techniques BibRef

Eisenberger, M.[Marvin], Toker, A.[Aysim], Leal-Taixé, L.[Laura], Bernard, F.[Florian], Cremers, D.[Daniel],
A Unified Framework for Implicit Sinkhorn Differentiation,
CVPR22(499-508)
IEEE DOI 2210
Training, Costs, Neural networks, Graphics processing units, Organizations, Approximation algorithms, Optimization methods, Machine learning BibRef

Wang, L.G.[Long-Guang], Dong, X.Y.[Xiao-Yu], Wang, Y.Q.[Ying-Qian], Liu, L.[Li], An, W.[Wei], Guo, Y.L.[Yu-Lan],
Learnable Lookup Table for Neural Network Quantization,
CVPR22(12413-12423)
IEEE DOI 2210
Training, Point cloud compression, Quantization (signal), Neural networks, Superresolution, Computational efficiency, Low-level vision BibRef

Shen, L.[Lulan], Ziaeefard, M.[Maryam], Meyer, B.[Brett], Gross, W.[Warren], Clark, J.J.[James J.],
Conjugate Adder Net (CAddNet) - a Space-Efficient Approximate CNN,
ECV22(2792-2796)
IEEE DOI 2210
Training, Deep learning, Neural networks, Logic gates, Complexity theory, Pattern recognition BibRef

Cho, Y.S.[Yoo-Shin], Cho, H.[Hanbyel], Kim, Y.S.[Young-Soo], Kim, J.[Junmo],
Improving Generalization of Batch Whitening by Convolutional Unit Optimization,
ICCV21(5301-5309)
IEEE DOI 2203
Transforming input features to have a zero mean and unit variance. Convolutional codes, Training, Correlation, Dogs, Transforms, Stability analysis, Decorrelation, Recognition and classification BibRef

Khani, M.[Mehrdad], Hamadanian, P.[Pouya], Nasr-Esfahany, A.[Arash], Alizadeh, M.[Mohammad],
Real-Time Video Inference on Edge Devices via Adaptive Model Streaming,
ICCV21(4552-4562)
IEEE DOI 2203
Performance evaluation, Training, Adaptation models, Computational modeling, Semantics, Bandwidth, Streaming media, grouping and shape BibRef

Liu, J.[Jie], Li, C.[Chuming], Liang, F.[Feng], Lin, C.[Chen], Sun, M.[Ming], Yan, J.J.[Jun-Jie], Ouyang, W.L.[Wan-Li], Xu, D.[Dong],
Inception Convolution with Efficient Dilation Search,
CVPR21(11481-11490)
IEEE DOI 2111
Image segmentation, Image recognition, Convolution, Pose estimation, Object detection, Performance gain, Pattern recognition 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.[Joăo],
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.Z.[Wei-Zhong], 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, Predictive models BibRef

Zhang, C.[Chen], Xu, Y.H.[Ying-Hao], 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.J.[Hao-Jin], 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.W.[Joey Yi-Wei],
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, Search problems 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, 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.L.[Ruo-Lin],
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

Zhang, L.,
Two recent advances on normalization methods for deep neural network optimization,
VCIP20(1-1)
IEEE DOI 2102
Training, Optimization, Neural networks, Standardization, Pattern recognition, Pattern analysis, Imaging BibRef

Du, K.Y.[Kun-Yuan], Zhang, Y.[Ya], Guan, H.B.[Hai-Bing], Tian, Q.[Qi], Wang, Y.F.[Yan-Feng], Cheng, S.G.[Sheng-Gan], Lin, J.[James],
FTL: A Universal Framework for Training Low-bit DNNs via Feature Transfer,
ECCV20(XXV:700-716).
Springer DOI 2011
BibRef

Jiang, Z.X.[Zi-Xuan], Zhu, K.[Keren], Liu, M.J.[Ming-Jie], Gu, J.Q.[Jia-Qi], Pan, D.Z.[David Z.],
An Efficient Training Framework for Reversible Neural Architectures,
ECCV20(XXVII:275-289).
Springer DOI 2011
Trade memory requirements for computation. BibRef

Herrmann, C.[Charles], Bowen, R.S.[Richard Strong], Zabih, R.[Ramin],
Channel Selection Using Gumbel Softmax,
ECCV20(XXVII:241-257).
Springer DOI 2011
Executing some layers, pruning, etc. BibRef

Isikdogan, L.F.[Leo F.], Nayak, B.V.[Bhavin V.], Wu, C.T.[Chyuan-Tyng], Moreira, J.P.[Joao Peralta], Rao, S.[Sushma], Michael, G.[Gilad],
SemifreddoNets: Partially Frozen Neural Networks for Efficient Computer Vision Systems,
ECCV20(XXVII:193-208).
Springer DOI 2011
Partial frozen weights. Only change some weights in learning. BibRef

Xie, X., Zhou, Y., Kung, S.Y.,
Exploring Highly Efficient Compact Neural Networks For Image Classification,
ICIP20(2930-2934)
IEEE DOI 2011
Convolution, Standards, Neural networks, Computational efficiency, Task analysis, Fuses, Lightweight network, inter-group information exchange BibRef

Ahn, S., Chang, J.W., Kang, S.J.,
An Efficient Accelerator Design Methodology For Deformable Convolutional Networks,
ICIP20(3075-3079)
IEEE DOI 2011
IP networks, Erbium, Zirconium, Indexes, Hardware accelerator, deformable convolution, system architecture, FPGA, deep learning BibRef

Kehrenberg, T.[Thomas], Bartlett, M.[Myles], Thomas, O.[Oliver], Quadrianto, N.[Novi],
Null-sampling for Interpretable and Fair Representations,
ECCV20(XXVI:565-580).
Springer DOI 2011
Code, CNN.
WWW Link. BibRef

Malkin, N.[Nikolay], Ortiz, A.[Anthony], Jojic, N.[Nebojsa],
Mining Self-similarity: Label Super-resolution with Epitomic Representations,
ECCV20(XXVI:531-547).
Springer DOI 2011
Learn from very large data-sets. BibRef

Liu, Z.G.[Zhi-Gang], Mattina, M.[Matthew],
Efficient Residue Number System Based Winograd Convolution,
ECCV20(XIX:53-68).
Springer DOI 2011
BibRef

Park, E.[Eunhyeok], Yoo, S.J.[Sung-Joo],
Profit: A Novel Training Method for sub-4-bit Mobilenet Models,
ECCV20(VI:430-446).
Springer DOI 2011
BibRef

Shomron, G.[Gil], Banner, R.[Ron], Shkolnik, M.[Moran], Weiser, U.[Uri],
Thanks for Nothing: Predicting Zero-valued Activations with Lightweight Convolutional Neural Networks,
ECCV20(X:234-250).
Springer DOI 2011
BibRef

Su, Z.[Zhuo], Fang, L.P.[Lin-Pu], Kang, W.X.[Wen-Xiong], Hu, D.[Dewen], Pietikäinen, M.[Matti], Liu, L.[Li],
Dynamic Group Convolution for Accelerating Convolutional Neural Networks,
ECCV20(VI:138-155).
Springer DOI 2011
BibRef

Xie, Z.D.[Zhen-Da], Zhang, Z.[Zheng], Zhu, X.[Xizhou], Huang, G.[Gao], Lin, S.[Stephen],
Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation,
ECCV20(I:531-548).
Springer DOI 2011
Reduci superfluous computation in feature maps of CNNs. BibRef

Phan, A.H.[Anh-Huy], Sobolev, K.[Konstantin], Sozykin, K.[Konstantin], Ermilov, D.[Dmitry], Gusak, J.[Julia], 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
BibRef

Yong, H.W.[Hong-Wei], Huang, J.Q.[Jian-Qiang], Hua, X.S.[Xian-Sheng], Zhang, L.[Lei],
Gradient Centralization: A New Optimization Technique for Deep Neural Networks,
ECCV20(I:635-652).
Springer DOI 2011
BibRef

Yuan, Z.N.[Zhuo-Ning], Guo, Z.S.[Zhi-Shuai], 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

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, Standards, Convolution, Inference algorithms BibRef

Elsen, E., Dukhan, M., Gale, T., Simonyan, K.,
Fast Sparse ConvNets,
CVPR20(14617-14626)
IEEE DOI 2008
Kernel, Sparse matrices, Neural networks, Standards, Computational modeling, Acceleration BibRef

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 BibRef

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, 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],
BlockCopy: High-Resolution Video Processing with Block-Sparse Feature Propagation and Online Policies,
ICCV21(5138-5147)
IEEE DOI 2203
Training, Image segmentation, Computational modeling, Semantics, Pipelines, Reinforcement learning, Video analysis and understanding 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, 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-Perpińán, 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, 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.H.[Christoph H.],
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
BibRef

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 BibRef

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 BibRef

Yan, X.P.[Xiao-Peng], Chen, Z.L.[Zi-Liang], Xu, A.[Anni], Wang, X.X.[Xiao-Xi], Liang, X.D.[Xiao-Dan], Lin, L.[Liang],
Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning,
ICCV19(9576-9585)
IEEE DOI 2004
Code, Learning.
HTML Version. convolutional neural nets, image representation, image sampling, image segmentation, Object recognition BibRef

Dai, X.L.[Xiao-Liang], Zhang, P.Z.[Pei-Zhao], Wu, B.[Bichen], Yin, H.X.[Hong-Xu], 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.T.[Matt T.], Jha, N.K.[Niraj K.],
ChamNet: Towards Efficient Network Design Through Platform-Aware Model Adaptation,
CVPR19(11390-11399).
IEEE DOI 2002
BibRef

Zhang, Y.F.[Yan-Fu], Gao, S.Q.[Shang-Qian], Huang, H.[Heng],
Exploration and Estimation for Model Compression,
ICCV21(477-486)
IEEE DOI 2203
Training, Visualization, Heuristic algorithms, Computational modeling, Estimation, Stochastic processes, Machine learning architectures and formulations BibRef

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
BibRef

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 2002
BibRef

Li, J.S.[Jia-Shi], 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 Networks,
CVPR19(7039-7048).
IEEE DOI 2002
BibRef

Kim, H.[Hyeji], Khan, M.U.K.[Muhammad Umar Karim], Kyung, C.M.[Chong-Min],
Efficient Neural Network Compression,
CVPR19(12561-12569).
IEEE DOI 2002
BibRef

Minnehan, B.[Breton], Savakis, A.[Andreas],
Cascaded Projection: End-To-End Network Compression and Acceleration,
CVPR19(10707-10716).
IEEE DOI 2002
BibRef

Lin, Y.H.[Yu-Hsun], Chou, C.N.[Chun-Nan], Chang, E.Y.[Edward Y.],
MBS: Macroblock Scaling for CNN Model Reduction,
CVPR19(9109-9117).
IEEE DOI 2002
BibRef

Gao, Y.[Yuan], Ma, J.Y.[Jia-Yi], Zhao, M.B.[Ming-Bo], Liu, W.[Wei], Yuille, A.L.[Alan L.],
NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction,
CVPR19(3200-3209).
IEEE DOI 2002
BibRef

Wang, H.Y.[Hui-Yu], Kembhavi, A.[Aniruddha], Farhadi, A.[Ali], Yuille, A.L.[Alan L.], Rastegari, M.[Mohammad],
ELASTIC: Improving CNNs With Dynamic Scaling Policies,
CVPR19(2253-2262).
IEEE DOI 2002
BibRef

Yang, H.C.[Hai-Chuan], Zhu, Y.H.[Yu-Hao], Liu, J.[Ji],
ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model,
CVPR19(11198-11207).
IEEE DOI 2002
BibRef

Gong, L.Y.[Li-Yu], Cheng, Q.A.[Qi-Ang],
Exploiting Edge Features for Graph Neural Networks,
CVPR19(9203-9211).
IEEE DOI 2002
BibRef

Kossaifi, J.[Jean], Bulat, A.[Adrian], Tzimiropoulos, G.[Georgios], Pantic, M.[Maja],
T-Net: Parametrizing Fully Convolutional Nets With a Single High-Order Tensor,
CVPR19(7814-7823).
IEEE DOI 2002
BibRef

Chen, W.J.[Wei-Jie], Xie, D.[Di], Zhang, Y.[Yuan], Pu, S.L.[Shi-Liang],
All You Need Is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification,
CVPR19(7234-7243).
IEEE DOI 2002
BibRef

Georgiadis, G.[Georgios],
Accelerating Convolutional Neural Networks via Activation Map Compression,
CVPR19(7078-7088).
IEEE DOI 2002
BibRef

Zhu, S.L.[Shi-Lin], Dong, X.[Xin], Su, H.[Hao],
Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?,
CVPR19(4918-4927).
IEEE DOI 2002
BibRef

Li, T.H.[Tuan-Hui], Wu, B.Y.[Bao-Yuan], Yang, Y.J.[Yu-Jiu], Fan, Y.B.[Yan-Bo], Zhang, Y.[Yong], Liu, W.[Wei],
Compressing Convolutional Neural Networks via Factorized Convolutional Filters,
CVPR19(3972-3981).
IEEE DOI 2002
BibRef

Kim, E.[Eunwoo], Ahn, C.[Chanho], Torr, P.H.S.[Philip H.S.], Oh, S.H.[Song-Hwai],
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks,
CVPR19(2705-2714).
IEEE DOI 2002
BibRef

He, T.[Tong], Zhang, Z.[Zhi], Zhang, H.[Hang], Zhang, Z.Y.[Zhong-Yue], Xie, J.Y.[Jun-Yuan], Li, M.[Mu],
Bag of Tricks for Image Classification with Convolutional Neural Networks,
CVPR19(558-567).
IEEE DOI 2002
BibRef

Wang, X.J.[Xi-Jun], Kan, M.[Meina], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Fully Learnable Group Convolution for Acceleration of Deep Neural Networks,
CVPR19(9041-9050).
IEEE DOI 2002
BibRef

Li, Y.C.[Yu-Chao], Lin, S.H.[Shao-Hui], Zhang, B.C.[Bao-Chang], Liu, J.Z.[Jian-Zhuang], Doermann, D.[David], Wu, Y.J.[Yong-Jian], Huang, F.Y.[Fei-Yue], Ji, R.R.[Rong-Rong],
Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression,
CVPR19(2795-2804).
IEEE DOI 2002
BibRef

Zhao, R.[Ritchie], Hu, Y.W.[Yu-Wei], Dotzel, J.[Jordan], de Sa, C.[Christopher], Zhang, Z.[Zhiru],
Building Efficient Deep Neural Networks With Unitary Group Convolutions,
CVPR19(11295-11304).
IEEE DOI 2002
BibRef

Qiao, S.Y.[Si-Yuan], Lin, Z.[Zhe], Zhang, J.M.[Jian-Ming], Yuille, A.L.[Alan L.],
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization,
CVPR19(61-71).
IEEE DOI 2002
BibRef

Tagaris, T.[Thanos], Sdraka, M.[Maria], Stafylopatis, A.[Andreas],
High-Resolution Class Activation Mapping,
ICIP19(4514-4518)
IEEE DOI 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

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, 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
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.C.[Ri-Chang], Qi, G.J.[Guo-Jun],
Interleaved Structured Sparse Convolutional Neural Networks,
CVPR18(8847-8856)
IEEE DOI 1812
Convolution, Kernel, Sparse matrices, Redundancy, Computational modeling, 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, Optimization, Redundancy BibRef

Bulň, 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
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, 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, 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, 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, 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
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
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, 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, 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 .


Last update:Mar 16, 2024 at 20:36:19