14.5.7.5.1 Convolutional Neural Networks, Design, Implementation Issues

Chapter Contents (Back)
Convolutional Neural Networks. Neural Networks. Deep Nets. CNN. Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible. See also Adversarial Networks, Adversarial Inputs.

Cao, Y.Q.[Yong-Qiang], Chen, Y.[Yang], Khosla, D.[Deepak],
Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition,
IJCV(113), No. 1, May 2015, pp. 54-66.
Springer DOI 1506
BibRef

Kim, I.J.[In-Jung], Choi, C.[Changbeom], Lee, S.H.[Sang-Heon],
Improving discrimination ability of convolutional neural networks by hybrid learning,
IJDAR(19), No. 1, March 2016, pp. 1-9.
WWW Link. 1602
BibRef

Xu, G., Wu, H.Z., Shi, Y.Q.,
Structural Design of Convolutional Neural Networks for Steganalysis,
SPLetters(23), No. 5, May 2016, pp. 708-712.
IEEE DOI 1604
Computer architecture BibRef

Nogueira, K.[Keiller], Penatti, O.A.B.[Otávio A.B.], dos Santos, J.A.[Jefersson A.],
Towards better exploiting convolutional neural networks for remote sensing scene classification,
PR(61), No. 1, 2017, pp. 539-556.
Elsevier DOI 1705
BibRef
Earlier: A2, A1, A3:
Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?,
EarthObserv15(44-51)
IEEE DOI 1510
Accuracy. Evaluation of convolutional networks. BibRef

Kuo, C.C.J.[C.C. Jay],
Understanding convolutional neural networks with a mathematical model,
JVCIR(41), No. 1, 2016, pp. 406-413.
Elsevier DOI 1612
Convolutional neural network (CNN) BibRef

Chellappa, R.[Rama],
The changing fortunes of pattern recognition and computer vision,
IVC(55, Part 1), No. 1, 2016, pp. 3-5.
Elsevier DOI 1612
Convolutional Neural Networks BibRef

Xu, C., Lu, C., Liang, X., Gao, J., Zheng, W., Wang, T., Yan, S.,
Multi-loss Regularized Deep Neural Network,
CirSysVideo(26), No. 12, December 2016, pp. 2273-2283.
IEEE DOI 1612
Computer architecture BibRef

Montavon, G.[Grégoire], Lapuschkin, S.[Sebastian], Binder, A.[Alexander], Samek, W.[Wojciech], Müller, K.R.[Klaus-Robert],
Explaining nonlinear classification decisions with deep Taylor decomposition,
PR(65), No. 1, 2017, pp. 211-222.
Elsevier DOI 1702
Deep neural networks BibRef

Lapuschkin, S., Binder, A., Montavon, G.[Grégoire], Müller, K.R.[Klaus-Robert], Samek, W.[Wojciech],
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks,
CVPR16(2912-2920)
IEEE DOI 1612
BibRef

Fan, J.P.[Jian-Ping], Zhao, T.Y.[Tian-Yi], Kuang, Z.Z.[Zhen-Zhong], Zheng, Y.[Yu], Zhang, J.[Ji], Yu, J.[Jun], Peng, J.Y.[Jin-Ye],
HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition,
IP(26), No. 4, April 2017, pp. 1923-1938.
IEEE DOI 1704
Atomic layer deposition. Large-scale. BibRef

Kuang, Z.Z.[Zhen-Zhong], Yu, J.[Jun], Li, Z.M.[Zong-Min], Zhang, B.P.[Bao-Peng], Fan, J.P.[Jian-Ping],
Integrating multi-level deep learning and concept ontology for large-scale visual recognition,
PR(78), 2018, pp. 198 - 214.
Elsevier DOI 1804
Large-scale visual recognition, Multi-level deep learning, Multiple deep networks, Concept ontology, Multi-task learning, Tree classifier BibRef

Du, B.[Bo], Xiong, W.[Wei], Wu, J.[Jia], Zhang, L.F.[Le-Fei], Zhang, L.P.[Liang-Pei], Tao, D.C.[Da-Cheng],
Stacked Convolutional Denoising Auto-Encoders for Feature Representation,
Cyber(47), No. 4, April 2017, pp. 1017-1027.
IEEE DOI 1704
Convolution BibRef

Geng, J., Wang, H., Fan, J., Ma, X.,
Deep Supervised and Contractive Neural Network for SAR Image Classification,
GeoRS(55), No. 4, April 2017, pp. 2442-2459.
IEEE DOI 1704
feature extraction BibRef

Geng, J., Wang, H., Fan, J., Ma, X.,
SAR Image Classification via Deep Recurrent Encoding Neural Networks,
GeoRS(56), No. 4, April 2018, pp. 2255-2269.
IEEE DOI 1804
Artificial neural networks, Feature extraction, Logic gates, Machine learning, Radar imaging, Synthetic aperture radar, synthetic aperture radar (SAR) image BibRef

Zhang, Z., Wang, H., Xu, F., Jin, Y.Q.,
Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification,
GeoRS(55), No. 12, December 2017, pp. 7177-7188.
IEEE DOI 1712
Computer vision, Convolution, Feature extraction, Machine learning, Neural networks, Synthetic aperture radar, Training, terrain classification BibRef

Malik, J.[Jitendra],
Technical Perspective: What Led Computer Vision to Deep Learning?,
CACM(60), No. 6, June 2017, pp. 82-83.
DOI Link 1706
Discusses next paper BibRef

Krizhevsky, A.[Alex], Sutskever, I.[Ilya], Hinton, G.E.[Geoffrey E.],
ImageNet Classification with Deep Convolutional Neural Networks,
CACM(60), No. 6, June 2017, pp. 84-90.
DOI Link 1706
Survey, Convolutional Networks. BibRef

Pan, X.Q.[Xia-Qing], Chen, Y.[Yueru], Kuo, C.C.J.[C.C. Jay],
Design, analysis and application of a volumetric convolutional neural network,
JVCIR(46), No. 1, 2017, pp. 128-138.
Elsevier DOI 1706
Convolutional, neural, network BibRef

Mishkin, D.[Dmytro], Sergievskiy, N.[Nikolay], Matas, J.[Jiri],
Systematic evaluation of convolution neural network advances on the Imagenet,
CVIU(161), No. 1, 2017, pp. 11-19.
Elsevier DOI 1708
CNN 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, Computer architecture, 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
Computer architecture, Computer vision, Feature extraction, Machine learning, Mobile communication, Neural networks, very large scale integration BibRef

Chen, Z.L.[Zhang-Ling], Wang, J.[Jun], Li, W.J.[Wen-Juan], Li, N.[Nan], Wu, H.M.[Hua-Ming], Wang, D.W.[Da-Wei],
Convolutional neural network with nonlinear competitive units,
SP:IC(60), No. 1, 2018, pp. 193-198.
Elsevier DOI 1712
Nonlinear competitive unit BibRef

Cui, Z., Niu, Z., Liu, L., Yan, S.,
Layerwise Class-Aware Convolutional Neural Network,
CirSysVideo(27), No. 12, December 2017, pp. 2601-2612.
IEEE DOI 1712
Biological neural networks, Computational modeling, Computer architecture, Convolutional codes, Mutual information, object classification BibRef

Mhalla, A.[Ala], Chateau, T.[Thierry], Maâmatou, H.[Houda], Gazzah, S.[Sami], Ben Amara, N.E.[Najoua Essoukri],
SMC faster R-CNN: Toward a scene-specialized multi-object detector,
CVIU(164), No. 1, 2017, pp. 3-15.
Elsevier DOI 1801
BibRef
Earlier: A1, A3, A2, A4, A5:
Faster R-CNN Scene Specialization with a Sequential Monte-Carlo Framework,
DICTA16(1-7)
IEEE DOI 1701
Transfer learning Approximation algorithms BibRef

Cheng, Y., Wang, D., Zhou, P., Zhang, T.,
Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges,
SPMag(35), No. 1, January 2018, pp. 126-136.
IEEE DOI 1801
Computational modeling, Convolution, Convolutional codes, Machine learning, Neural networks, Quantization (signal), Training data BibRef

Boulch, A.[Alexandre],
Reducing parameter number in residual networks by sharing weights,
PRL(103), 2018, pp. 53-59.
Elsevier DOI 1802
BibRef

Liang, P.[Peng], Shi, W.Z.[Wen-Zhong], Zhang, X.K.[Xiao-Kang],
Remote Sensing Image Classification Based on Stacked Denoising Autoencoder,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
Train then add noise and train. BibRef

Lee, C.Y.[Chen-Yu], Gallagher, P.[Patrick], Tu, Z.W.[Zhuo-Wen],
Generalizing Pooling Functions in CNNs: Mixed, Gated, and Tree,
PAMI(40), No. 4, April 2018, pp. 863-875.
IEEE DOI 1804
computational complexity, feedforward neural nets, learning (artificial intelligence), neural net architecture, supervised classification BibRef

Li, X.[Xin], Jie, Z.[Zequn], Feng, J.[Jiashi], Liu, C.S.[Chang-Song], Yan, S.C.[Shui-Cheng],
Learning with rethinking: Recurrently improving convolutional neural networks through feedback,
PR(79), 2018, pp. 183-194.
Elsevier DOI 1804
Convolutional neural network, Image classification, Deep learning BibRef

Zhang, K.[Ke], Sun, M.[Miao], Han, T.X.[Tony X.], Yuan, X.F.[Xing-Fang], Guo, L.[Liru], Liu, T.[Tao],
Residual Networks of Residual Networks: Multilevel Residual Networks,
CirSysVideo(28), No. 6, June 2018, pp. 1303-1314.
IEEE DOI 1806
How to stack networks for real problems. Computer architecture, Neural networks, Optimization, Road transportation, Stochastic processes, Sun, Training, stochastic depth (SD) BibRef

Akilan, T.[Thangarajah], Wu, Q.M.J.[Qing-Ming Jonathan], Zhang, H.[Hui],
Effect of fusing features from multiple DCNN architectures in image classification,
IET-IPR(12), No. 7, July 2018, pp. 1102-1110.
DOI Link 1806
BibRef

Ye, J., Han, Y., Cha, E.,
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems,
SIIMS(11), No. 2, 2018, pp. 991-1048.
DOI Link 1807
BibRef

Fu, R.G.[Rui-Gang], Li, B.[Biao], Gao, Y.[Yinghui], Wang, P.[Ping],
CNN with coarse-to-fine layer for hierarchical classification,
IET-CV(12), No. 6, September 2018, pp. 892-899.
DOI Link 1808
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

Gui, L.Y., Gui, L., Wang, Y.X., Morency, L.P., Moura, J.M.F.,
Factorized Convolutional Networks: Unsupervised Fine-Tuning for Image Clustering,
WACV18(1205-1214)
IEEE DOI 1806
convolution, feedforward neural nets, image recognition, image representation, matrix decomposition, pattern clustering, Tuning BibRef

Mittal, D., Bhardwaj, S., Khapra, M.M., Ravindran, B.,
Recovering from Random Pruning: On the Plasticity of Deep Convolutional Neural Networks,
WACV18(848-857)
IEEE DOI 1806
image classification, learning (artificial intelligence), neural nets, object detection, RCNN model, class specific pruning, Tuning BibRef

Prabhu, A.[Ameya], Batchu, V.[Vishal], Munagala, S.A.[Sri Aurobindo], Gajawada, R.[Rohit], Namboodiri, A.[Anoop],
Distribution-Aware Binarization of Neural Networks for Sketch Recognition,
WACV18(830-838)
IEEE DOI 1806
computer vision, data compression, edge detection, image coding, learning (artificial intelligence), neural nets, Task analysis BibRef

Prabhu, A.[Ameya], Batchu, V.[Vishal], Gajawada, R.[Rohit], Munagala, S.A.[Sri Aurobindo], Namboodiri, A.[Anoop],
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory,
WACV18(821-829)
IEEE DOI 1806
approximation theory, data compression, image classification, image coding, image representation, Quantization (signal) BibRef

Follmann, P., Bottger, T.,
A Rotationally-Invariant Convolution Module by Feature Map Back-Rotation,
WACV18(784-792)
IEEE DOI 1806
convolution, feature extraction, feedforward neural nets, image classification, learning (artificial intelligence), CNNs, Transforms BibRef

Huang, Q., Zhou, K., You, S., Neumann, U.,
Learning to Prune Filters in Convolutional Neural Networks,
WACV18(709-718)
IEEE DOI 1806
computer vision, image segmentation, learning (artificial intelligence), neural nets, CNN filters, Training BibRef

Edwards, M.[Michael], Xie, X.H.[Xiang-Hua],
Graph Convolutional Neural Network,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Guo, Y.M.[Yan-Ming], Lew, M.S.[Michael S.],
Bag of Surrogate Parts: one inherent feature of deep CNNs,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Zhao, S.W.[Shan-Wei], Zhao, Z.C.[Zhi-Cheng], Su, F.[Fei],
Gram matrix based representation for image retrieval,
VCIP17(1-4)
IEEE DOI 1804
Second order features based on convolutional layers. feedforward neural nets, image coding, image representation, image retrieval, matrix algebra, Gram matrix, image retrieval BibRef

Yim, J., Sohn, K.A.,
Enhancing the Performance of Convolutional Neural Networks on Quality Degraded Datasets,
DICTA17(1-8)
IEEE DOI 1804
convolution, image classification, image filtering, learning (artificial intelligence), neural nets, Noise reduction BibRef

An, W., Wang, H., Zhang, Y., Dai, Q.,
Exponential decay sine wave learning rate for fast deep neural network training,
VCIP17(1-4)
IEEE DOI 1804
gradient methods, image classification, learning (artificial intelligence), neural nets, optimisation, optimization BibRef

Gupta, K.[Kavya], Majumdar, A.[Angshul],
Learning autoencoders with low-rank weights,
ICIP17(3899-3903)
IEEE DOI 1803
Artificial neural networks, Biological neural networks, Decoding, Neurons, Noise reduction, Redundancy, Training, autoencoder, nuclear norm BibRef

Huang, M.Y.[Mou-Yue], Lai, C.H.[Ching-Hao], Chen, S.H.[Sin-Horng],
Fast and accurate image recognition using Deeply-Fused Branchy Networks,
ICIP17(2876-2880)
IEEE DOI 1803
Agriculture, Collaboration, Error analysis, Graphics processing units, Image recognition, Network topology, inference time BibRef

Chadha, A., Abbas, A., Andreopoulos, Y.,
Compressed-domain video classification with deep neural networks: 'There's way too much information to decode the matrix',
ICIP17(1832-1836)
IEEE DOI 1803
Neural networks, Optical imaging, Optical network units, Standards, Training, classification, video coding BibRef

Bochinski, E., Senst, T., Sikora, T.,
Hyper-parameter optimization for convolutional neural network committees based on evolutionary algorithms,
ICIP17(3924-3928)
IEEE DOI 1803
Error analysis, Evolutionary computation, Kernel, Optimization, Sociology, Statistics, Training, Convolutional Neural Network, MNIST BibRef

Dominguez, M., Such, F.P., Sah, S., Ptucha, R.,
Towards 3D convolutional neural networks with meshes,
ICIP17(3929-3933)
IEEE DOI 1803
Convolution, Convolutional neural networks, Feature extraction, Graph theory, Tensile stress, voxels BibRef

Wang, Z., Zhu, C., Xia, Z., Guo, Q., Liu, Y.,
Towards thinner convolutional neural networks through gradually global pruning,
ICIP17(3939-3943)
IEEE DOI 1803
Computational modeling, Machine learning, Measurement, Neurons, Redundancy, Tensile stress, Training, Artificial neural networks, Deep learning BibRef

Yoshiyasu, Y., Yoshida, E., Pirk, S., Guibas, L.,
3D convolutional neural networks by modal fusion,
ICIP17(1777-1781)
IEEE DOI 1803
Encoding, Robots, Shape, Solid modeling, Testing, BibRef

Pasupuleti, S.K., Miniskar, N.R., Rajagopal, V., Gadde, R.N.,
A novel method to regenerate an optimal CNN by exploiting redundancy patterns in the network,
ICIP17(4407-4411)
IEEE DOI 1803
Complexity theory, Computational modeling, Convolution, Kernel, Neural networks, Redundancy, Semantics, Caffe, light-weight network BibRef

Dahia, G., Santos, M., Segundo, M.P.,
A study of CNN outside of training conditions,
ICIP17(3820-3824)
IEEE DOI 1803
Color, Databases, Face, Face recognition, Image color analysis, Machine learning, Training, CNNs, Deep Learning, Face Recognition BibRef

Jeon, S.R.[Sang-Ryul], Kim, S.R.[Seung-Ryong], Sohn, K.H.[Kwang-Hoon],
Convolutional feature pyramid fusion via attention network,
ICIP17(1007-1011)
IEEE DOI 1803
Computer architecture, Estimation, Feature extraction, Optical imaging, Robustness, Semantics, Visualization, feature pyramid BibRef

Ishii, M.[Masato], Sato, A.[Atsushi],
Layer-Wise Weight Decay for Deep Neural Networks,
PSIVT17(276-289).
Springer DOI 1802
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

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

Zhong, Y., Ettinger, G.,
Enlightening Deep Neural Networks with Knowledge of Confounding Factors,
CEFR-LCV17(1077-1086)
IEEE DOI 1802
Biological neural networks, Data models, Neurons, Object recognition, Training 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

Gupta, A., Duggal, R.,
P-TELU: Parametric Tan Hyperbolic Linear Unit Activation for Deep Neural Networks,
CEFR-LCV17(974-978)
IEEE DOI 1802
Benchmark testing, Biological neural networks, Computer architecture, Convergence, Neurons, Noise robustness, Standards BibRef

Worrall, D.E.[Daniel E.], Garbin, S.J.[Stephan J.], Turmukhambetov, D.[Daniyar], Brostow, G.J.[Gabriel J.],
Interpretable Transformations with Encoder-Decoder Networks,
ICCV17(5737-5746)
IEEE DOI 1802
I.e. rotation effects. Explain results. decoding, image coding, interpolation, transforms, complex transformation encoding, BibRef

Kolkin, N., Shakhnarovich, G., Shechtman, E.,
Training Deep Networks to be Spatially Sensitive,
ICCV17(5669-5678)
IEEE DOI 1802
Spatial issues. approximation theory, computational complexity, gradient methods, image denoising, image segmentation, Training BibRef

Oyallon, E.[Edouard], Belilovsky, E.[Eugene], Zagoruyko, S.[Sergey],
Scaling the Scattering Transform: Deep Hybrid Networks,
ICCV17(5619-5628)
IEEE DOI 1802
Initialization of the network. convolution, image coding, neural nets, transforms, Deep CNNs, Deep hybrid networks, Resnet-18 architecture, Wavelet transforms BibRef

Yu, A.[Aron], Grauman, K.[Kristen],
Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images,
ICCV17(5571-5580)
IEEE DOI 1802
Augment real training images by artivicial noisy images. image processing, learning (artificial intelligence), dense supervision, fashion images, semantic jitter, Visualization BibRef

Marcos, D.[Diego], Volpi, M.[Michele], Komodakis, N.[Nikos], Tuia, D.[Devis],
Rotation Equivariant Vector Field Networks,
ICCV17(5058-5067)
IEEE DOI 1802
CNN encoding roataion invariance. convolution, filtering theory, image segmentation, learning (artificial intelligence), medical image processing, BibRef

Luo, J.H., Wu, J., Lin, W.,
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression,
ICCV17(5068-5076)
IEEE DOI 1802
data compression, image coding, image filtering, inference mechanisms, neural nets, optimisation, Training BibRef

Simon, M.[Marcel], Gao, Y.[Yang], Darrell, T.J.[Trevor J.], Denzler, J.[Joachim], Rodner, E.[Erik],
Generalized Orderless Pooling Performs Implicit Salient Matching,
ICCV17(4970-4979)
IEEE DOI 1802
CNN. Learn the pooling strategy also. feature extraction, feedforward neural nets, image classification, image matching, image representation, Visualization BibRef

Zhang, T.[Ting], Qi, G.J.[Guo-Jun], Xiao, B.[Bin], Wang, J.D.[Jing-Dong],
Interleaved Group Convolutions,
ICCV17(4383-4392)
IEEE DOI 1802
Modularized NN. convolution, filtering theory, group theory, image classification, learning (artificial intelligence), neural nets, Tensile stress BibRef

Zhang, F.H.[Fei-Hu], Wah, B.W.[Benjamin W.],
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks,
ICCV17(4354-4363)
IEEE DOI 1802
Network results depend on image. image classification, image resolution, learning (artificial intelligence), neural nets, MLNN, SNN, Training BibRef

Sankaranarayanan, S.[Swami], Jain, A.[Arpit], Lim, S.N.[Ser Nam],
Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks,
ICCV17(3582-3590)
IEEE DOI 1802
Perturb the inputs, understand NN results. Explain. image classification, image representation, neural nets, CIFAR10 datasets, MNIST, PASCAL VOC dataset, Semantics BibRef

Morerio, P.[Pietro], Cavazza, J.[Jacopo], Volpi, R.[Riccardo], Vidal, R.[René], Murino, V.[Vittorio],
Curriculum Dropout,
ICCV17(3564-3572)
IEEE DOI 1802
Remove NN units to reduce over-specific detectors. feature extraction, generalisation (artificial intelligence), image classification, image representation, Training BibRef

Wang, G., Xie, X., Lai, J., Zhuo, J.,
Deep Growing Learning,
ICCV17(2831-2839)
IEEE DOI 1802
convolution, data handling, learning (artificial intelligence), neural nets, DGL, SSL framework, deep growing learning, deep network, Visualization BibRef

Kamiya, R., Yamashita, T., Ambai, M., Sato, I., Yamauchi, Y., Fujiyoshi, H.,
Binary-Decomposed DCNN for Accelerating Computation and Compressing Model Without Retraining,
CEFR-LCV17(1095-1102)
IEEE DOI 1802
Acceleration, Approximation algorithms, Computational modeling, Image recognition, Matrix decomposition, Quantization (signal) BibRef

Zhangy, Y., Ozayy, M., Li, S., Okatani, T.,
Truncating Wide Networks Using Binary Tree Architectures,
ICCV17(2116-2124)
IEEE DOI 1802
image classification, learning (artificial intelligence), neural nets, pattern classification, trees (mathematics), Vegetation BibRef

Li, Y.H.[Yang-Hao], Wang, N.Y.[Nai-Yan], Liu, J.Y.[Jia-Ying], Hou, X.D.[Xiao-Di],
Factorized Bilinear Models for Image Recognition,
ICCV17(2098-2106)
IEEE DOI 1802
added layer to CNN. convolution, image recognition, image representation, learning (artificial intelligence), matrix decomposition, Training BibRef

Wang, Y.[Yan], Xie, L.X.[Ling-Xi], Liu, C.X.[Chen-Xi], Qiao, S.Y.[Si-Yuan], Zhang, Y.[Ya], Zhang, W.J.[Wen-Jun], Tian, Q.[Qi], Yuille, A.L.[Alan L.],
SORT: Second-Order Response Transform for Visual Recognition,
ICCV17(1368-1377)
IEEE DOI 1802
Second order operators in deep networks. image recognition, neural nets, transforms, SORT, Second-Order Response Transform, chain-styled network, Visualization BibRef

Dai, J.F.[Ji-Feng], Qi, H.Z.[Hao-Zhi], Xiong, Y.[Yuwen], Li, Y.[Yi], Zhang, G.D.[Guo-Dong], Hu, H.[Han], Wei, Y.C.[Yi-Chen],
Deformable Convolutional Networks,
ICCV17(764-773)
IEEE DOI 1802
convolution, feedforward neural nets, image segmentation, learning (artificial intelligence), 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

Selvaraju, R.R.[Ramprasaath R.], Cogswell, M.[Michael], Das, A.[Abhishek], Vedantam, R.[Ramakrishna], Parikh, D.[Devi], Batra, D.[Dhruv],
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,
ICCV17(618-626)
IEEE DOI 1802
Explain the CNN models. convolution, data visualisation, gradient methods, image classification, image representation, inference mechanisms, Visualization BibRef

Wen, W., Xu, C., Wu, C., Wang, Y., Chen, Y., Li, H.,
Coordinating Filters for Faster Deep Neural Networks,
ICCV17(658-666)
IEEE DOI 1802
computer vision, image classification, image filtering, learning (artificial intelligence), neural nets, Tensile stress BibRef

Osherov, E., Lindenbaum, M.,
Increasing CNN Robustness to Occlusions by Reducing Filter Support,
ICCV17(550-561)
IEEE DOI 1802
image classification, image filtering, learning (artificial intelligence), neural nets, Weight measurement BibRef

Cecconi, L.[Leonardo], Smets, S.[Sander], Benini, L.[Luca], Verhelst, M.[Marian],
Optimal Tiling Strategy for Memory Bandwidth Reduction for CNNs,
ACIVS17(89-100).
Springer DOI 1712
BibRef

Rueda, F.M.[Fernando Moya], Grzeszick, R.[Rene], Fink, G.A.[Gernot A.],
Neuron Pruning for Compressing Deep Networks Using Maxout Architectures,
GCPR17(177-188).
Springer DOI 1711
BibRef

Ceruti, C.[Claudio], Campadelli, P.[Paola], Casiraghi, E.[Elena],
Linear Regularized Compression of Deep Convolutional Neural Networks,
CIAP17(I:244-253).
Springer DOI 1711
BibRef

Kobler, E.[Erich], Klatzer, T.[Teresa], Hammernik, K.[Kerstin], Pock, T.[Thomas],
Variational Networks: Connecting Variational Methods and Deep Learning,
GCPR17(281-293).
Springer DOI 1711
BibRef

Wang, F.[Fei], Jiang, M.Q.[Meng-Qing], Qian, C.[Chen], Yang, S.[Shuo], Li, C.[Cheng], Zhang, H.G.[Hong-Gang], Wang, X.G.[Xiao-Gang], Tang, X.[Xiaoou],
Residual Attention Network for Image Classification,
CVPR17(6450-6458)
IEEE DOI 1711
Image color analysis, Logic gates, Neural networks, Noise measurement, Stacking, Training BibRef

Han, D.[Dongyoon], Kim, J.[Jiwhan], Kim, J.[Junmo],
Deep Pyramidal Residual Networks,
CVPR17(6307-6315)
IEEE DOI 1711
Additives, Artificial neural networks, Feature extraction, Network, architecture BibRef

Yang, T.J.[Tien-Ju], Chen, Y.H.[Yu-Hsin], Sze, V.[Vivienne],
Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning,
CVPR17(6071-6079)
IEEE DOI 1711
Computational modeling, Energy consumption, Estimation, Hardware, Measurement, Memory management, Smart, phones BibRef

Xie, D., Xiong, J., Pu, S.,
All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation,
CVPR17(5075-5084)
IEEE DOI 1711
Convolution, Jacobian matrices, Modulation, Network architecture, Neural networks, Training BibRef

Zhai, S.F.[Shuang-Fei], Wu, H.[Hui], Kumar, A.[Abhishek], Cheng, Y.[Yu], Lu, Y.X.[Yong-Xi], Zhang, Z.F.[Zhong-Fei], Feris, R.[Rogerio],
S3Pool: Pooling with Stochastic Spatial Sampling,
CVPR17(4003-4011)
IEEE DOI 1711
Convolution, Distortion, Feature extraction, Neural networks, Standards, Stochastic, processes BibRef

Chen, B.H.[Bing-Hui], Deng, W.H.[Wei-Hong], Du, J.P.[Jun-Ping],
Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation,
CVPR17(4021-4030)
IEEE DOI 1711
Annealing, Noise measurement, Robustness, Standards, Telecommunications, Training BibRef

Wei, Z.[Zhen], Sun, Y.[Yao], Wang, J.Q.[Jin-Qiao], Lai, H.J.[Han-Jiang], Liu, S.[Si],
Learning Adaptive Receptive Fields for Deep Image Parsing Network,
CVPR17(3947-3955)
IEEE DOI 1711
Face, Interpolation, Kernel, Manuals, Training BibRef

Wang, P.S.[Pei-Song], Cheng, J.[Jian],
Fixed-Point Factorized Networks,
CVPR17(3966-3974)
IEEE DOI 1711
DNN. Acceleration, Computational modeling, Matrix decomposition, Neural networks, Quantization, (signal) BibRef

Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.B.,
Network Dissection: Quantifying Interpretability of Deep Visual Representations,
CVPR17(3319-3327)
IEEE DOI 1711
Detectors, Image color analysis, Image segmentation, Semantics, Training, Visualization BibRef

Patrini, G.[Giorgio], Rozza, A.[Alessandro], Menon, A.K.[Aditya Krishna], Nock, R.[Richard], Qu, L.Z.[Li-Zhen],
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach,
CVPR17(2233-2241)
IEEE DOI 1711
Clothing, Computer architecture, Neural networks, Noise measurement, Robustness, Training BibRef

Jeon, Y.[Yunho], Kim, J.[Junmo],
Active Convolution: Learning the Shape of Convolution for Image Classification,
CVPR17(1846-1854)
IEEE DOI 1711
Convolution, Convolutional codes, Interpolation, Lattices, Neurons, Shape BibRef

Chollet, F.,
Xception: Deep Learning with Depthwise Separable Convolutions,
CVPR17(1800-1807)
IEEE DOI 1711
Biological neural networks, Computer architecture, Convolutional codes, Correlation, BibRef

Zamir, A.R., Wu, T.L., Sun, L., Shen, W.B., Shi, B.E., Malik, J., Savarese, S.,
Feedback Networks,
CVPR17(1808-1817)
IEEE DOI 1711
Computer architecture, Feedforward systems, Logic gates, Microprocessors, Predictive models, Taxonomy BibRef

Figurnov, M.[Michael], Collins, M.D.[Maxwell D.], Zhu, Y.K.[Yu-Kun], Zhang, L.[Li], Huang, J.[Jonathan], Vetrov, D.[Dmitry], Salakhutdinov, R.[Ruslan],
Spatially Adaptive Computation Time for Residual Networks,
CVPR17(1790-1799)
IEEE DOI 1711
Adaptation models, Computational modeling, Computer architecture, Feature extraction, Image segmentation, Object detection BibRef

Harley, A.W., Derpanis, K.G., Kokkinos, I.,
Segmentation-Aware Convolutional Networks Using Local Attention Masks,
ICCV17(5048-5057)
IEEE DOI 1802
convolution, filtering theory, image segmentation, learning (artificial intelligence), neural nets, Semantics BibRef

Kokkinos, I.,
UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory,
CVPR17(5454-5463)
IEEE DOI 1711
Computer architecture, Discrete wavelet transforms, Estimation, Proposals, Semantics, Training BibRef

Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.,
Aggregated Residual Transformations for Deep Neural Networks,
CVPR17(5987-5995)
IEEE DOI 1711
Complexity theory, Computer architecture, Network topology, Neural networks, Neurons, Topology 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

Cui, Y., Zhou, F., Wang, J., Liu, X., Lin, Y., Belongie, S.J.[Serge J.],
Kernel Pooling for Convolutional Neural Networks,
CVPR17(3049-3058)
IEEE DOI 1711
Kernel, Neural networks, Taylor series, Tensile stress, Training, Visualization BibRef

Yu, F.[Fisher], Koltun, V.[Vladlen], Funkhouser, T.[Thomas],
Dilated Residual Networks,
CVPR17(636-644)
IEEE DOI 1711
Convolution, Image segmentation, Semantics, Spatial resolution, Training BibRef

Bagherinezhad, H., Rastegari, M., Farhadi, A.,
LCNN: Lookup-Based Convolutional Neural Network,
CVPR17(860-869)
IEEE DOI 1711
Computational modeling, Dictionaries, Machine learning, Neural networks, Solid modeling, Tensile stress, Training BibRef

Juefei-Xu, F.[Felix], Boddeti, V.N., Savvides, M.[Marios],
Local Binary Convolutional Neural Networks,
CVPR17(4284-4293)
IEEE DOI 1711
Computational modeling, Convolution, Encoding, Neural networks, Standards, Training BibRef

Juefei-Xu, F.[Felix], Savvides, M.[Marios],
Learning to Invert Local Binary Patterns,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Kossaifi, J.[Jean], Khanna, A.[Aran], Lipton, Z.[Zachary], Furlanello, T.[Tommaso], Anandkumar, A.[Anima],
Tensor Contraction Layers for Parsimonious Deep Nets,
Tensor17(1940-1946)
IEEE DOI 1709
Complexity theory, Computer vision, Tensile stress BibRef

Guo, J.[Jia], Potkonjak, M.[Miodrag],
Pruning ConvNets Online for Efficient Specialist Models,
ECVW17(430-437)
IEEE DOI 1709
Biological neural networks, Computational modeling, Computer vision, Convolution, Memory management, Sensitivity, analysis 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

Liu, X., Li, S., Kan, M., Shan, S., Chen, X.,
Self-Error-Correcting Convolutional Neural Network for Learning with Noisy Labels,
FG17(111-117)
IEEE DOI 1707
Biological neural networks, Face, Neurons, Noise measurement, Noise robustness, Switches, Training BibRef

Araújo, T.[Teresa], Aresta, G.[Guilherme], Almada-Lobo, B.[Bernardo], Mendonça, A.M.[Ana Maria], Campilho, A.[Aurélio],
Improving Convolutional Neural Network Design via Variable Neighborhood Search,
ICIAR17(371-379).
Springer DOI 1706
BibRef

Ibrahim, A.[Ahmed], Abbott, A.L.[A. Lynn], Hussein, M.E.[Mohamed E.],
Input Fast-Forwarding for Better Deep Learning,
ICIAR17(363-370).
Springer DOI 1706
BibRef

Hernández, G.[Gerardo], Zamora, E.[Erik], Sossa, H.[Humberto],
Comparing Deep and Dendrite Neural Networks: A Case Study,
MCPR17(32-41).
Springer DOI 1706
BibRef

Xu, X., Todorovic, S.,
Beam search for learning a deep Convolutional Neural Network of 3D shapes,
ICPR16(3506-3511)
IEEE DOI 1705
Computational modeling, Computer architecture, Knowledge transfer, Shape, Solid modeling, Three-dimensional displays, Training BibRef

Gwon, Y.[Youngjune], Cha, M.[Miriam], Kung, H.T.,
Deep Sparse-coded Network (DSN),
ICPR16(2610-2615)
IEEE DOI 1705
Backpropagation, Computer architecture, Dictionaries, Encoding, Neural networks, Nonhomogeneous media, Training BibRef

Teerapittayanon, S., McDanel, B., Kung, H.T.,
BranchyNet: Fast inference via early exiting from deep neural networks,
ICPR16(2464-2469)
IEEE DOI 1705
Entropy, Feedforward neural networks, Inference algorithms, Optimization, Runtime, Training BibRef

Pham, T., Tran, T., Phung, D., Venkatesh, S.,
Faster training of very deep networks via p-norm gates,
ICPR16(3542-3547)
IEEE DOI 1705
Computer architecture, Feedforward neural networks, Logic gates, Road transportation, Standards, Training BibRef

Tabernik, D.[Domen], Kristan, M.[Matej], Wyatt, J.L., Leonardis, A.[Aleš],
Towards deep compositional networks,
ICPR16(3470-3475)
IEEE DOI 1705
Computational modeling, Convolution, Cost function, Mathematical model, Neural networks, Standards, Visualization BibRef

Kabkab, M., Hand, E., Chellappa, R.,
On the size of Convolutional Neural Networks and generalization performance,
ICPR16(3572-3577)
IEEE DOI 1705
Boolean functions, Databases, Feedforward neural networks, Probability distribution, Testing, Training BibRef

Uchida, K., Tanaka, M., Okutomi, M.,
Coupled convolution layer for convolutional neural network,
ICPR16(3548-3553)
IEEE DOI 1705
Cells (biology), Convolution, Optical imaging, Photonics, Photoreceptors, Retina, Training BibRef

Wang, Y.Q.[Ye-Qing], Li, Y.[Yi], Porikli, F.M.[Fatih M.],
Finetuning Convolutional Neural Networks for visual aesthetics,
ICPR16(3554-3559)
IEEE DOI 1705
Computer vision, Feature extraction, Machine learning, Neural networks, Semantics, Training, Visualization, Deep learning, visual, aesthetics BibRef

Burlina, P.,
MRCNN: A stateful Fast R-CNN,
ICPR16(3518-3523)
IEEE DOI 1705
Bayes methods, Filtering, Graphics processing units, Object detection, Proposals, Target tracking, ConvNets, Deep Learning, Fast R-CNN, Region CNNs in Video, Region, Proposals BibRef

Tobías, L., Ducournau, A., Rousseau, F., Mercier, G., Fablet, R.,
Convolutional Neural Networks for object recognition on mobile devices: A case study,
ICPR16(3530-3535)
IEEE DOI 1705
Biological neural networks, Computational modeling, Computer architecture, Feature extraction, Kernel, Mobile handsets, Training, Convolutional Neural Networks, Deep Learning, Machine Learning, Mobile Devices, Object, Detection BibRef

Afridi, M.J., Ross, A., Shapiro, E.M.,
L-CNN: Exploiting labeling latency in a CNN learning framework,
ICPR16(2156-2161)
IEEE DOI 1705
Biomedical imaging, Computer architecture, Labeling, Magnetic resonance imaging, Microprocessors, Testing, Training BibRef

Ghaderi, A., Athitsos, V.,
Selective unsupervised feature learning with Convolutional Neural Network (S-CNN),
ICPR16(2486-2490)
IEEE DOI 1705
Classification algorithms, Convolutional codes, Kernel, Neural networks, Search problems, Support vector machines, Training, Artificial Neural Networks, Classification and Clustring, Deep, Learning BibRef

Käding, C.[Christoph], Rodner, E.[Erik], Freytag, A.[Alexander], Denzler, J.[Joachim],
Fine-Tuning Deep Neural Networks in Continuous Learning Scenarios,
DeepVisual16(III: 588-605).
Springer DOI 1704
BibRef

Kirillov, A., Schlesinger, D., Zheng, S., Savchynskyy, B., Torr, P.H.S.[Philip H.S.], Rother, C.,
Joint Training of Generic CNN-CRF Models with Stochastic Optimization,
ACCV16(II: 221-236).
Springer DOI 1704
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

Gao, Y., Liu, Z., Wang, D.,
Error models of finite word length arithmetic in CNN accelerator design,
VCIP16(1-4)
IEEE DOI 1701
Analytical models BibRef

Shaheen, F.[Fatma], Verma, B.[Brijesh], Asafuddoula, M.,
Impact of Automatic Feature Extraction in Deep Learning Architecture,
DICTA16(1-8)
IEEE DOI 1701
Biological neural networks BibRef

Mao, F.L.[Feng-Ling], Xiong, W.[Wei], Du, B.[Bo], Zhang, L.[Lefei],
Stochastic Decorrelation Constraint Regularized Auto-Encoder for Visual Recognition,
MMMod17(II: 368-380).
Springer DOI 1701
BibRef

Liu, Y.[Yu], Guo, Y.M.[Yan-Ming], Bakker, E.M., Lew, M.S.[Michael S.],
Learning a Recurrent Residual Fusion Network for Multimodal Matching,
ICCV17(4127-4136)
IEEE DOI 1802
image matching, image representation, learning (artificial intelligence), text analysis, RRF, Visualization BibRef

Liu, Y.[Yu], Guo, Y.M.[Yan-Ming], Lew, M.S.[Michael S.],
On the Exploration of Convolutional Fusion Networks for Visual Recognition,
MMMod17(I: 277-289).
Springer DOI 1701
BibRef

Ujiie, T., Hiromoto, M., Sato, T.,
Approximated Prediction Strategy for Reducing Power Consumption of Convolutional Neural Network Processor,
ECVW16(870-876)
IEEE DOI 1612
BibRef

Honari, S.[Sina], Yosinski, J.[Jason], Vincent, P.[Pascal], Pal, C.[Christopher],
Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation,
CVPR16(5743-5752)
IEEE DOI 1612
BibRef

Hu, P.Y.[Pei-Yun], Ramanan, D.[Deva],
Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians,
CVPR16(5600-5609)
IEEE DOI 1612
BibRef

Lavin, A.[Andrew], Gray, S.[Scott],
Fast Algorithms for Convolutional Neural Networks,
CVPR16(4013-4021)
IEEE DOI 1612
BibRef

Misra, I.[Ishan], Shrivastava, A.[Abhinav], Gupta, A.[Abhinav], Hebert, M.[Martial],
Cross-Stitch Networks for Multi-task Learning,
CVPR16(3994-4003)
IEEE DOI 1612
Learn shared representations. BibRef

Hu, H.X.[He-Xiang], Zhou, G.T.[Guang-Tong], Deng, Z.W.[Zhi-Wei], Liao, Z.C.[Zi-Cheng], Mori, G.[Greg],
Learning Structured Inference Neural Networks with Label Relations,
CVPR16(2960-2968)
IEEE DOI 1612
Network for each layer of representation. BibRef

Szegedy, C.[Christian], Vanhoucke, V.[Vincent], Ioffe, S.[Sergey], Shlens, J.[Jon], Wojna, Z.[Zbigniew],
Rethinking the Inception Architecture for Computer Vision,
CVPR16(2818-2826)
IEEE DOI 1612
scale-up CNN recognition to larger number of classes. 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

Jain, A.[Ashesh], Zamir, A.R.[Amir R.], Savarese, S.[Silvio], Saxena, A.[Ashutosh],
Structural-RNN: Deep Learning on Spatio-Temporal Graphs,
CVPR16(5308-5317)
IEEE DOI 1612
Award, CVPR, Student. BibRef

Xie, L., Wang, J., Wei, Z., Wang, M., Tian, Q.,
DisturbLabel: Regularizing CNN on the Loss Layer,
CVPR16(4753-4762)
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

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

Cohen, N., Sharir, O., Shashua, A.,
Deep SimNets,
CVPR16(4782-4791)
IEEE DOI 1612
BibRef

Shankar, S., Robertson, D., Ioannou, Y., Criminisi, A., Cipolla, R.[Roberto],
Refining Architectures of Deep Convolutional Neural Networks,
CVPR16(2212-2220)
IEEE DOI 1612
BibRef

Chen, H.G., Jayasuriya, S., Yang, J., Stephen, J., Sivaramakrishnan, S., Veeraraghavan, A., Molnar, A.,
ASP Vision: Optically Computing the First Layer of Convolutional Neural Networks Using Angle Sensitive Pixels,
CVPR16(903-912)
IEEE DOI 1612
BibRef

Wang, J.D.[Jing-Dong], Yuille, A.L.[Alan L.], Tian, Q.[Qi],
InterActive: Inter-Layer Activeness Propagation,
CVPR16(270-279)
IEEE DOI 1612
BibRef

Jacobsen, J.H., van Gemert, J.C.[Jan C.], Lou, Z., Smeulders, A.W.M.,
Structured Receptive Fields in CNNs,
CVPR16(2610-2619)
IEEE DOI 1612
BibRef

Lebedev, V., Lempitsky, V.,
Fast ConvNets Using Group-Wise Brain Damage,
CVPR16(2554-2564)
IEEE DOI 1612
BibRef

Rastegar, S., Baghshah, M.S.[Mahdieh Soleymani], Rabiee, H.R.[Hamid R.], Shojaee, S.M.,
MDL-CW: A Multimodal Deep Learning Framework with CrossWeights,
CVPR16(2601-2609)
IEEE DOI 1612
BibRef

Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.,
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks,
CVPR16(2574-2582)
IEEE DOI 1612
BibRef

Wu, J.X.[Jia-Xiang], Leng, C.[Cong], Wang, Y.H.[Yu-Hang], Hu, Q.H.[Qing-Hao], Cheng, J.[Jian],
Quantized Convolutional Neural Networks for Mobile Devices,
CVPR16(4820-4828)
IEEE DOI 1612
Implementations. BibRef

Xie, L.X.[Ling-Xi], Tian, Q.[Qi], Flynn, J.[John], Wang, J.D.[Jing-Dong], Yuille, A.L.[Alan L.],
Geometric Neural Phrase Pooling: Modeling the Spatial Co-Occurrence of Neurons,
ECCV16(I: 645-661).
Springer DOI 1611
BibRef

Jin, X.J.[Xiao-Jie], Chen, Y.P.[Yun-Peng], Dong, J.[Jian], Feng, J.[Jiashi], Yan, S.C.[Shui-Cheng],
Collaborative Layer-Wise Discriminative Learning in Deep Neural Networks,
ECCV16(VII: 733-749).
Springer DOI 1611
BibRef

Goo, W.[Wonjoon], Kim, J.Y.[Ju-Yong], Kim, G.[Gunhee], Hwang, S.J.[Sung Ju],
Taxonomy-Regularized Semantic Deep Convolutional Neural Networks,
ECCV16(II: 86-101).
Springer DOI 1611
BibRef

Shen, L.[Li], Lin, Z.C.[Zhou-Chen], Huang, Q.M.[Qing-Ming],
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks,
ECCV16(VII: 467-482).
Springer DOI 1611
BibRef

Wang, Z.Y.[Zhen-Yang], Deng, Z.D.[Zhi-Dong], Wang, S.[Shiyao],
Accelerating Convolutional Neural Networks with Dominant Convolutional Kernel and Knowledge Pre-regression,
ECCV16(VIII: 533-548).
Springer DOI 1611
BibRef

Zhou, H.[Hao], Alvarez, J.M.[Jose M.], Porikli, F.M.[Fatih M.],
Less Is More: Towards Compact CNNs,
ECCV16(IV: 662-677).
Springer DOI 1611
BibRef

Shrivastava, A.[Abhinav], Gupta, A.[Abhinav],
Contextual Priming and Feedback for Faster R-CNN,
ECCV16(I: 330-348).
Springer DOI 1611
BibRef

Yu, D.[Dan], Wu, X.J.[Xiao-Jun],
VLAD Is not Necessary for CNN,
TASKCV16(III: 492-499).
Springer DOI 1611
BibRef

Bach, S.[Sebastian], Binder, A.[Alexander], Müller, K.R.[Klaus-Robert], Samek, W.[Wojciech],
Controlling explanatory heatmap resolution and semantics via decomposition depth,
ICIP16(2271-2275)
IEEE DOI 1610
Computational modeling BibRef

Pang, J., Lin, H., Su, L., Zhang, C., Zhang, W., Duan, L., Huang, Q., Yin, B.,
Accelerate convolutional neural networks for binary classification via cascading cost-sensitive feature,
ICIP16(1037-1041)
IEEE DOI 1610
Acceleration 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

Carvalho, M., Cord, M., Avila, S., Thome, N., Valle, E.,
Deep neural networks under stress,
ICIP16(4443-4447)
IEEE DOI 1610
Computational modeling 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

Liao, Z., Carneiro, G.,
On the importance of normalisation layers in deep learning with piecewise linear activation units,
WACV16(1-8)
IEEE DOI 1606
Data models 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

Yang, Z.C.[Zi-Chao], Moczulski, M.[Marcin], Denil, M.[Misha], de Freitas, N.[Nando], Smola, A.J.[Alexander J.], Song, L.[Le], Wang, Z.Y.[Zi-Yu],
Deep Fried Convnets,
ICCV15(1476-1483)
IEEE DOI 1602
Adaptive systems BibRef

Ionescu, C., Vantzos, O., Sminchisescu, C.,
Matrix Backpropagation for Deep Networks with Structured Layers,
ICCV15(2965-2973)
IEEE DOI 1602
Backpropagation BibRef

Yang, B., Yan, J., Lei, Z., Li, S.Z.,
Convolutional Channel Features,
ICCV15(82-90)
IEEE DOI 1602
Boosting BibRef

Girshick, R.,
Fast R-CNN,
ICCV15(1440-1448)
IEEE DOI 1602
Computer architecture 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
Convolutional Neural Networks for Object Detection and Segmentation .


Last update:Aug 16, 2018 at 18:22:30