14.5.9.7.13 Deep Learning, Deep Nets

Bengio, Y.,
Learning deep architectures for AI,
FTML(1), No. 1, 2009, pp. 1-127.
DOI Link 1609
BibRef

Zhao, W.Z.[Wen-Zhi], Du, S.H.[Shi-Hong],
Learning multiscale and deep representations for classifying remotely sensed imagery,
PandRS(113), No. 1, 2016, pp. 155-165.
Elsevier DOI 1602
Multiscale convolutional neural network (MCNN) BibRef

Barat, C.[Cécile], Ducottet, C.[Christophe],
String representations and distances in deep Convolutional Neural Networks for image classification,
PR(54), No. 1, 2016, pp. 104-115.
Elsevier DOI 1603
Convolutional Neural Network BibRef

Greenspan, H., van Ginneken, B., Summers, R.M.,
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique,
MedImg(35), No. 5, May 2016, pp. 1153-1159.
IEEE DOI 1605
Artificial neural networks BibRef

Ghesu, F.C.[Florin C.], Georgescu, B., Zheng, Y., Grbic, S., Maier, A., Hornegger, J.[Joachim], Comaniciu, D.,
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans,
PAMI(41), No. 1, January 2019, pp. 176-189.
IEEE DOI 1812
Machine learning, Biomedical imaging, Search problems, Training, Real-time systems, Deep learning, intelligent localization BibRef

Murthy, V.N., Singh, V., Chen, T., Manmatha, R., Comaniciu, D.,
Deep Decision Network for Multi-class Image Classification,
CVPR16(2240-2248)
IEEE DOI 1612
BibRef

Mansoor, A., Cerrolaza, J.J., Idrees, R., Biggs, E., Alsharid, M.A., Avery, R.A., Linguraru, M.G.,
Deep Learning Guided Partitioned Shape Model for Anterior Visual Pathway Segmentation,
MedImg(35), No. 8, August 2016, pp. 1856-1865.
IEEE DOI 1608
Biomedical optical imaging BibRef

van Noord, N.[Nanne], Postma, E.[Eric],
Learning scale-variant and scale-invariant features for deep image classification,
PR(61), No. 1, 2017, pp. 583-592.
Elsevier DOI 1609
Convolutional Neural Networks BibRef

Du, J.[Jun], Xu, Y.[Yong],
Hierarchical deep neural network for multivariate regression,
PR(63), No. 1, 2017, pp. 149-157.
Elsevier DOI 1612
Divide and Conquer BibRef

Guo, S., Huang, W., Wang, L., Qiao, Y.,
Locally Supervised Deep Hybrid Model for Scene Recognition,
IP(26), No. 2, February 2017, pp. 808-820.
IEEE DOI 1702
data compression BibRef

Masoumi, M.[Majid], Ben Hamza, A.,
Spectral shape classification: A deep learning approach,
JVCIR(43), No. 1, 2017, pp. 198-211.
Elsevier DOI 1702
Deep learning BibRef

Luciano, L.[Lorenzo], Ben Hamza, A.,
Deep learning with geodesic moments for 3D shape classification,
PRL(105), 2018, pp. 182-190.
Elsevier DOI 1804
Geodesic moments, Deep learning, Laplace-Beltrami, Stacked autoencoders, Shape classification BibRef

Luciano, L.[Lorenzo], Ben Hamza, A.,
Deep similarity network fusion for 3D shape classification,
VC(35), No. 6-8, June 2018, pp. 1171-1180.
WWW Link. 1906
BibRef

Li, Y.[Yao], Liu, L.Q.[Ling-Qiao], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Mining Mid-level Visual Patterns with Deep CNN Activations,
IJCV(121), No. 3, February 2017, pp. 344-364.
Springer DOI 1702
BibRef

Sun, W.C.[Wei-Chen], Su, F.[Fei],
A novel companion objective function for regularization of deep convolutional neural networks,
IVC(60), No. 1, 2017, pp. 58-63.
Elsevier DOI 1704
BibRef
Earlier:
Regularization of deep neural networks using a novel companion objective function,
ICIP15(2865-2869)
IEEE DOI 1512
Convolutional neural network. Companion objective function BibRef

Sun, W.C.[Wei-Chen], Su, F.[Fei], Wang, L.Q.[Lei-Quan],
Improving deep neural networks with multilayer maxout networks,
VCIP14(334-337)
IEEE DOI 1504
filtering theory BibRef

Wu, F.[Fei], Wang, Z.H.[Zhu-Hao], Lu, W.M.[Wei-Ming], Li, X.[Xi], Yang, Y.[Yi], Luo, J.B.[Jie-Bo], Zhuang, Y.T.[Yue-Ting],
Regularized Deep Belief Network for Image Attribute Detection,
CirSysVideo(27), No. 7, July 2017, pp. 1464-1477.
IEEE DOI 1707
Computational modeling, Context modeling, Correlation, Feature extraction, Neural networks, Semantics, Training, Contextual correlation, deep belief network (DBN), deep learning, image, attribute BibRef

Lee, H., Kwon, H.,
Going Deeper With Contextual CNN for Hyperspectral Image Classification,
IP(26), No. 10, October 2017, pp. 4843-4855.
IEEE DOI 1708
geophysical image processing, hyperspectral imaging, image classification, neural nets, CNN-based hyperspectral image classification, contextual deep CNN, joint spatio-spectral feature map, local contextual interactions, multiscale convolutional filter bank, Biological neural networks, Feature extraction, Hyperspectral imaging, Principal component analysis, Training, hyperspectral image classification, multi-scale filter bank, BibRef

Shi, C.[Cheng], Pun, C.M.[Chi-Man],
Superpixel-based 3D deep neural networks for hyperspectral image classification,
PR(74), No. 1, 2018, pp. 600-616.
Elsevier DOI 1711
Hyperspectral image classification BibRef

Shi, C.[Cheng], Pun, C.M.[Chi-Man],
Multiscale Superpixel-Based Hyperspectral Image Classification Using Recurrent Neural Networks With Stacked Autoencoders,
MultMed(22), No. 2, February 2020, pp. 487-501.
IEEE DOI 2001
Recurrent neural networks, Correlation, Feature extraction, Neurons, Hyperspectral imaging, Principal component analysis, Stacked autoencoders BibRef

Wang, K.[Keze], Zhang, D.Y.[Dong-Yu], Li, Y.[Ya], Zhang, R.M.[Rui-Mao], Lin, L.[Liang],
Cost-Effective Active Learning for Deep Image Classification,
CirSysVideo(27), No. 12, December 2017, pp. 2591-2600.
IEEE DOI 1712
Labeling, Learning systems, Measurement uncertainty, Neural networks, Training, Uncertainty, Visualization, incremental learning BibRef

Zagoruyko, S.[Sergey], Komodakis, N.[Nikos],
Deep compare: A study on using convolutional neural networks to compare image patches,
CVIU(164), No. 1, 2017, pp. 38-55.
Elsevier DOI 1801
BibRef
Earlier:
Learning to compare image patches via convolutional neural networks,
CVPR15(4353-4361)
IEEE DOI 1510
Descriptor learning BibRef

Simonovsky, M.[Martin], Komodakis, N.[Nikos],
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs,
CVPR17(29-38)
IEEE DOI 1711
Convolution, Machine learning, Neural networks, Spectral analysis, Standards, BibRef

Simonovsky, M.[Martin], Komodakis, N.[Nikos],
OnionNet: Sharing Features in Cascaded Deep Classifiers,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Liu, N.[Na], Lu, X.[Xiankai], Wan, L.[Lihong], Huo, H.[Hong], Fang, T.[Tao],
Improving the Separability of Deep Features with Discriminative Convolution Filters for RSI Classification,
IJGI(7), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Liu, Y.F.[Yan-Fei], Zhong, Y.F.[Yan-Fei], Fei, F.[Feng], Zhu, Q.[Qiqi], Qin, Q.Q.[Qian-Qing],
Scene Classification Based on a Deep Random-Scale Stretched Convolutional Neural Network,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Liu, Y.F.[Yan-Fei], Zhong, Y.F.[Yan-Fei], Qin, Q.Q.[Qian-Qing],
Scene Classification Based on Multiscale Convolutional Neural Network,
GeoRS(56), No. 12, December 2018, pp. 7109-7121.
IEEE DOI 1812
Feature extraction, Remote sensing, Semantics, Convolutional neural networks, Training, Machine learning, similarity measure BibRef

Zhou, Y.C.[Yu-Can], Hu, Q.H.[Qing-Hua], Wang, Y.[Yu],
Deep super-class learning for long-tail distributed image classification,
PR(80), 2018, pp. 118-128.
Elsevier DOI 1805
Super-class construction, Block-structured sparsity, Deep learning, Long-tail distribution BibRef

Liu, Y.[Yu], Liu, L.[Li], Guo, Y.M.[Yan-Ming], Lew, M.S.[Michael S.],
Learning visual and textual representations for multimodal matching and classification,
PR(84), 2018, pp. 51-67.
Elsevier DOI 1809
Vision and language, Multimodal matching, Multimodal classification, Deep learning BibRef

Ben Hamida, A., Benoit, A., Lambert, P., Ben Amar, C.,
3-D Deep Learning Approach for Remote Sensing Image Classification,
GeoRS(56), No. 8, August 2018, pp. 4420-4434.
IEEE DOI 1808
geophysical image processing, hyperspectral imaging, image classification, learning (artificial intelligence), remote sensing (RS) BibRef

Piramanayagam, S.[Sankaranarayanan], Saber, E.[Eli], Schwartzkopf, W.[Wade], Koehler, F.W.[Frederick W.],
Supervised Classification of Multisensor Remotely Sensed Images Using a Deep Learning Framework,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Mountrakis, G.[Giorgos], Li, J.[Jun], Lu, X.Q.[Xiao-Qiang], Hellwich, O.[Olaf],
Deep learning for remotely sensed data,
PandRS(145), 2018, pp. 1-2.
Elsevier DOI 1810
BibRef

Achille, A.[Alessandro], Soatto, S.[Stefano],
Information Dropout: Learning Optimal Representations Through Noisy Computation,
PAMI(40), No. 12, December 2018, pp. 2897-2905.
IEEE DOI 1811
Neural networks, Deep learning, Bayes methods, Machine learning, Information theory, Noise measurement, Learning systems, minimality BibRef

Xu, J., Wang, C., Qi, C., Shi, C., Xiao, B.,
Unsupervised Semantic-Based Aggregation of Deep Convolutional Features,
IP(28), No. 2, February 2019, pp. 601-611.
IEEE DOI 1811
feedforward neural nets, image classification, image representation, image retrieval, unsupervised learning, semantic detectors BibRef

Chevalier, M.[Marion], Thome, N.[Nicolas], Hénaff, G.[Gilles], Cord, M.[Matthieu],
Classifying low-resolution images by integrating privileged information in deep CNNs,
PRL(116), 2018, pp. 29-35.
Elsevier DOI 1812
Image classification, Deep convolutional neural networks, Learning using privileged information. BibRef

Zhang, Z.X.[Zhao-Xiang], Shan, S.G.[Shi-Guang], Fang, Y.[Yi], Shao, L.[Ling],
Deep Learning for Pattern Recognition,
PRL(119), 2019, pp. 1-2.
Elsevier DOI 1902
BibRef

Wang, J.[Jia], Liu, C.[Chen], Fu, T.[Tian], Zheng, L.[Lili],
Research on automatic target detection and recognition based on deep learning,
JVCIR(60), 2019, pp. 44-50.
Elsevier DOI 1903
Image processing, Target detection, Target recognition, In-depth learning BibRef

Zhang, J.[Ji], Mei, K.[Kuizhi], Zheng, Y.[Yu], Fan, J.P.[Jian-Ping],
Learning multi-layer coarse-to-fine representations for large-scale image classification,
PR(91), 2019, pp. 175-189.
Elsevier DOI 1904
Visual-semantic tree, Inter-category correlation, Multi-task learning, Deep convolutional neural network, Large-scale image classification BibRef

Gao, Z.[Zhi], Wu, Y.[Yuwei], Bu, X.Y.[Xing-Yuan], Yu, T.[Tan], Yuan, J.S.[Jun-Song], Jia, Y.D.[Yun-De],
Learning a robust representation via a deep network on symmetric positive definite manifolds,
PR(92), 2019, pp. 1-12.
Elsevier DOI 1905
Feature aggregation, SPD Matrix, Riemannian manifold, Deep convolutional network BibRef

Ding, G.G.[Gui-Guang], Guo, Y.C.[Yu-Chen], Chen, K.[Kai], Chu, C.Q.[Chao-Qun], Han, J.G.[Jun-Gong], Dai, Q.G.[Qion-Ghai],
DECODE: Deep Confidence Network for Robust Image Classification,
IP(28), No. 8, August 2019, pp. 3752-3765.
IEEE DOI 1907
convolutional neural nets, data visualisation, image classification, image denoising, confidence model BibRef

Zheng, X.T.[Xiang-Tao], Yuan, Y.[Yuan], Lu, X.Q.[Xiao-Qiang],
A Deep Scene Representation for Aerial Scene Classification,
GeoRS(57), No. 7, July 2019, pp. 4799-4809.
IEEE DOI 1907
Feature extraction, Encoding, Strain, Task analysis, Remote sensing, Training, Semantics, Aerial scene classification, multiscale representation BibRef

Liu, D., Cheng, B., Wang, Z., Zhang, H., Huang, T.S.,
Enhance Visual Recognition Under Adverse Conditions via Deep Networks,
IP(28), No. 9, Sep. 2019, pp. 4401-4412.
IEEE DOI 1908
image recognition, image restoration, learning (artificial intelligence), neural nets, image recognition BibRef

Cai, Z.[Ziyun], Long, Y.[Yang], Shao, L.[Ling],
Classification complexity assessment for hyper-parameter optimization,
PRL(125), 2019, pp. 396-403.
Elsevier DOI 1909
Hyper-parameter optimization, Deep learning, Classification complexity measure BibRef

Liao, Z.B.[Zhi-Bin], Drummond, T.[Tom], Reid, I.D.[Ian D.], Carneiro, G.[Gustavo],
Approximate Fisher Information Matrix to Characterize the Training of Deep Neural Networks,
PAMI(42), No. 1, January 2020, pp. 15-26.
IEEE DOI 1912
Training, Machine learning, Neural networks, Computational modeling, Convergence, Linear programming, Testing, neural network training characterisation BibRef

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

Zhuang, B., Lin, G., Shen, C., Reid, I.D.,
Fast Training of Triplet-Based Deep Binary Embedding Networks,
CVPR16(5955-5964)
IEEE DOI 1612
BibRef

Lyu, K., Li, Y., Zhang, Z.,
Attention-Aware Multi-Task Convolutional Neural Networks,
IP(29), No. 1, 2020, pp. 1867-1878.
IEEE DOI 1912
Task analysis, Deep learning, Feature extraction, Training, Estimation, Semantics, Convolutional neural networks, representation sharing BibRef

Li, Y.Y.[Yu-Yuan], Zhang, D.[Dong], Lee, D.J.[Dah-Jye],
IIRNet: A lightweight deep neural network using intensely inverted residuals for image recognition,
IVC(92), 2019, pp. 103819.
Elsevier DOI 1912
Convolutional neural network (CNN), Lightweight CNN, Image recognition, Low-redundancy, Model size, Computation complexity BibRef

He, C.[Chu], Zhang, Q.Y.[Qing-Yi], Qu, T.[Tao], Wang, D.W.[Ding-Wen], Liao, M.S.[Ming-Sheng],
Remote Sensing and Texture Image Classification Network Based on Deep Learning Integrated with Binary Coding and Sinkhorn Distance,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Tsiligianni, E., Deligiannis, N.,
Deep Coupled-Representation Learning for Sparse Linear Inverse Problems With Side Information,
SPLetters(26), No. 12, December 2019, pp. 1768-1772.
IEEE DOI 2001
computational complexity, image reconstruction, image representation, inverse problems, designing deep neural networks BibRef

Guo, J.[Jun], Yuan, X.[Xuan], Xu, P.F.[Peng-Fei], Bai, H.[Hao], Liu, B.[Baoying],
Improved image clustering with deep semantic embedding,
PRL(130), 2020, pp. 225-233.
Elsevier DOI 2002
Semantic embedding, Image clustering, Deep neural networks, Deep autoencoder BibRef

Kim, E.Y.[Eu Young], Shin, S.Y.[Seung Yeon], Lee, S.[Soochahn], Lee, K.J.[Kyong Joon], Lee, K.H.[Kyoung Ho], Lee, K.M.[Kyoung Mu],
Triplanar convolution with shared 2D kernels for 3D classification and shape retrieval,
CVIU(193), 2020, pp. 102901.
Elsevier DOI 2003
3D vision, Medical image, Deep learning BibRef

Yavartanoo, M.[Mohsen], Hung, S.H.[Shih-Hsuan], Neshatavar, R.[Reyhaneh], Zhang, Y.[Yue], Lee, K.M.[Kyoung Mu],
PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation,
3DV21(1014-1023)
IEEE DOI 2201
Convolutional codes, Geometry, Deep learning, Image segmentation, Shape, Aggregates BibRef

Yavartanoo, M.[Mohsen], Kim, E.Y.[Eu Young], Lee, K.M.[Kyoung Mu],
SPNet: Deep 3D Object Classification and Retrieval Using Stereographic Projection,
ACCV18(V:691-706).
Springer DOI 1906
BibRef

Do, T.T.[Thanh-Toan], Hoang, T.[Tuan], Tan, D.K.L.[Dang-Khoa Le], Doan, A.D.[Anh-Dzung], Cheung, N.M.[Ngai-Man],
Compact Hash Code Learning With Binary Deep Neural Network,
MultMed(22), No. 4, April 2020, pp. 992-1004.
IEEE DOI 2004
Binary constraint optimization, image search, learning to hash BibRef

Do, T.T.[Thanh-Toan], Hoang, T.[Tuan], Tan, D.K.L.[Dang-Khoa Le], Pham, T.[Trung], Le, H.[Huu], Cheung, N.M.[Ngai-Man], Reid, I.D.[Ian D.],
Binary Constrained Deep Hashing Network for Image Retrieval Without Manual Annotation,
WACV19(695-704)
IEEE DOI 1904
binary codes, image representation, image retrieval, neural nets, compact representation methods, image retrieval, Feature extraction BibRef

Do, T.T.[Thanh-Toan], Doan, A.D.[Anh-Dzung], Cheung, N.M.[Ngai-Man],
Learning to Hash with Binary Deep Neural Network,
ECCV16(V: 219-234).
Springer DOI 1611
BibRef

Tan, D.K.L.[Dang-Khoa Le], Do, T.T.[Thanh-Toan], Cheung, N.M.[Ngai-Man],
Supervised Hashing with End-to-End Binary Deep Neural Network,
ICIP18(3019-3023)
IEEE DOI 1809
Feature extraction, Training, Binary codes, Testing, Visualization, Image retrieval, Optimization, deep neural network, BibRef

Ruthotto, L.[Lars], Haber, E.[Eldad],
Deep Neural Networks Motivated by Partial Differential Equations,
JMIV(62), No. 3, April 2020, pp. 352-364.
Springer DOI 2004
BibRef

Forcén, J.I.[Juan Ignacio], Pagola, M.[Miguel], Barrenechea, E.[Edurne], Bustince, H.[Humberto],
Co-occurrence of deep convolutional features for image search,
IVC(97), 2020, pp. 103909.
Elsevier DOI 2005
BibRef
Earlier:
Aggregation of Deep Features for Image Retrieval Based on Object Detection,
IbPRIA19(I:553-564).
Springer DOI 1910
Image retrieval, Feature aggregation, Pooling BibRef

Zhang, D.W.[Ding-Wen], Han, J.W.[Jun-Wei], Zhang, Y.[Yu], Xu, D.[Dong],
Synthesizing Supervision for Learning Deep Saliency Network without Human Annotation,
PAMI(42), No. 7, July 2020, pp. 1755-1769.
IEEE DOI 2006
Object detection, Detectors, Training, Knowledge engineering, Task analysis, Semantics, Feature extraction, weakly supervised semantic segmentation BibRef

Passalis, N.[Nikolaos], Raitoharju, J.[Jenni], Tefas, A.[Anastasios], Gabbouj, M.[Moncef],
Efficient adaptive inference for deep convolutional neural networks using hierarchical early exits,
PR(105), 2020, pp. 107346.
Elsevier DOI 2006
Adaptive inference, Early exits, Bag-of-Features, Deep convolutional neural networks, Hierarchical representations BibRef

Huang, L.[Lei], Liu, X.L.[Xiang-Long], Qin, J.[Jie], Zhu, F.[Fan], Liu, L.[Li], Shao, L.[Ling],
Projection based weight normalization: Efficient method for optimization on oblique manifold in DNNs,
PR(105), 2020, pp. 107317.
Elsevier DOI 2006
Deep learning, Weight normalization, Oblique manifold, Image classification BibRef

Cococcioni, M.[Marco], Rossi, F.[Federico], Ruffaldi, E.[Emanuele], Saponara, S.[Sergio],
Fast deep neural networks for image processing using posits and ARM scalable vector extension,
RealTimeIP(17), No. 3, June 2020, pp. 759-771.
Springer DOI 2006
BibRef

Tanaka, M.[Masayuki],
Weighted sigmoid gate unit for an activation function of deep neural network,
PRL(135), 2020, pp. 354-359.
Elsevier DOI 2006
Deep neural network, Activation function, Relu BibRef

Ulyanov, D.[Dmitry], Vedaldi, A.[Andrea], Lempitsky, V.[Victor],
Deep Image Prior,
IJCV(128), No. 7, July 2020, pp. 1867-1888.
Springer DOI 2007
show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. BibRef

Shin, D., Yoo, H.,
The Heterogeneous Deep Neural Network Processor With a Non-von Neumann Architecture,
PIEEE(108), No. 8, August 2020, pp. 1245-1260.
IEEE DOI 2007
Computer architecture, Hardware, Biological neural networks, Computers, Recurrent neural networks, Deep learning, recurrent neural networks (RNNs) BibRef

Zhu, R., Dornaika, F., Ruichek, Y.,
Semi-supervised elastic manifold embedding with deep learning architecture,
PR(107), 2020, pp. 107425.
Elsevier DOI 2008
Graph-based embedding, Elastic embedding, Deep learning architecture, Supervised learning, Semi-supervised learning BibRef

Amjad, R.A.[Rana Ali], Geiger, B.C.[Bernhard C.],
Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle,
PAMI(42), No. 9, September 2020, pp. 2225-2239.
IEEE DOI 2008
Training, Task analysis, Robustness, Cost function, Neurons, Neural networks, Deep learning, information bottleneck, stochastic neural networks BibRef

Chen, Z.[Zhi], Ho, P.H.[Pin-Han],
A generic shift-norm-activation approach for deep learning,
PR(109), 2021, pp. 107609.
Elsevier DOI 2009
Activation, Normalization, CNN, Shifting, Deep learning BibRef

Pretorius, A.[Arnu], Kamper, H.[Herman], Kroon, S.[Steve],
On the expected behaviour of noise regularised deep neural networks as Gaussian processes,
PRL(138), 2020, pp. 75-81.
Elsevier DOI 2010
Neural networks, Gaussian processes, Signal propagation, Noise regularisation BibRef

Santiago, C.[Carlos], Barata, C.[Catarina], Sasdelli, M.[Michele], Carneiro, G.[Gustavo], Nascimento, J.C.[Jacinto C.],
LOW: Training deep neural networks by learning optimal sample weights,
PR(110), 2021, pp. 107585.
Elsevier DOI 2011
Deep learning, Sample weighting, Imbalanced data sets BibRef

Dixit, M.[Mandar], Li, Y.S.[Yun-Sheng], Vasconcelos, N.M.[Nuno M.],
Semantic Fisher Scores for Task Transfer: Using Objects to Classify Scenes,
PAMI(42), No. 12, December 2020, pp. 3102-3118.
IEEE DOI 2011
BibRef
And: A2, A1, A3:
Deep Scene Image Classification with the MFAFVNet,
ICCV17(5757-5765)
IEEE DOI 1802
Semantics, Neural networks, Training data, Object recognition, Neural networks, Computational modeling, Probability, MFA. Computational modeling, Covariance matrices, Feature extraction, Training BibRef

Chen, G.[Gang], Srihari, S.N.[Sargur N.],
Revisiting hierarchy: Deep learning with orthogonally constrained prior for classification,
PRL(140), 2020, pp. 214-221.
Elsevier DOI 2012
Hierarchical prior, Classification, Deep learning BibRef

Luo, P.[Ping], Zhang, R.M.[Rui-Mao], Ren, J.M.[Jia-Min], Peng, Z.L.[Zhang-Lin], Li, J.Y.[Jing-Yu],
Switchable Normalization for Learning-to-Normalize Deep Representation,
PAMI(43), No. 2, February 2021, pp. 712-728.
IEEE DOI 2101
Task analysis, Training, Graphics processing units, Switches, Object detection, Image segmentation, Head, Deep learning, semantic segmentation and face verification BibRef

Fukushima, K.,
Artificial Vision by Deep CNN Neocognitron,
SMCS(51), No. 1, January 2021, pp. 76-90.
IEEE DOI 2101
Visualization, Focusing, Visual systems, Biology, Pattern recognition, Convolutional neural networks, History, selective attention BibRef

Qi, Z.A.[Zhong-Ang], Khorram, S.[Saeed], Fuxin, L.[Li],
Embedding deep networks into visual explanations,
AI(292), 2021, pp. 103435.
Elsevier DOI 2102
Deep neural networks, Embedding, Visual explanations BibRef

Messina, N.[Nicola], Amato, G.[Giuseppe], Carrara, F.[Fabio], Gennaro, C.[Claudio], Falchi, F.[Fabrizio],
Solving the same-different task with convolutional neural networks,
PRL(143), 2021, pp. 75-80.
Elsevier DOI 2102
AI, Deep learning, Abstract reasoning, Relational reasoning, Convolutional neural, Networks BibRef

Amato, G.[Giuseppe], Carrara, F.[Fabio], Falchi, F.[Fabrizio], Gennaro, C.[Claudio], Lagani, G.[Gabriele],
Hebbian Learning Meets Deep Convolutional Neural Networks,
CIAP19(I:324-334).
Springer DOI 1909
BibRef

Yan, Z.[Ziang], Guo, Y.[Yiwen], Zhang, C.S.[Chang-Shui],
Adversarial Margin Maximization Networks,
PAMI(43), No. 4, April 2021, pp. 1129-1139.
IEEE DOI 2103
Perturbation methods, Training, Support vector machines, Distortion, Radio frequency, Robustness, Neural networks, deep neural networks BibRef

Li, S.[Shuai], Jia, K.[Kui], Wen, Y.X.[Yu-Xin], Liu, T.L.[Tong-Liang], Tao, D.C.[Da-Cheng],
Orthogonal Deep Neural Networks,
PAMI(43), No. 4, April 2021, pp. 1352-1368.
IEEE DOI 2103
Training, Robustness, Jacobian matrices, Task analysis, Neural networks, Optimization, Deep learning, Deep neural networks, image classification BibRef

Cai, H.Y.[Hua-Yue], Zhang, X.[Xiang], Lan, L.[Long], Dong, G.H.[Guo-Hua], Xu, C.F.[Chuan-Fu], Liu, X.W.[Xin-Wang], Luo, Z.G.[Zhi-Gang],
Learning deep discriminative embeddings via joint rescaled features and log-probability centers,
PR(114), 2021, pp. 107852.
Elsevier DOI 2103
Deep discriminative embedding, Softmax loss, Easing overfitting BibRef

Kortylewski, A.[Adam], Liu, Q.[Qing], Wang, A.T.[Ang-Tian], Sun, Y.H.[Yi-Hong], Yuille, A.L.[Alan L.],
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition Under Occlusion,
IJCV(129), No. 3, March 2021, pp. 736-760.
Springer DOI 2103
BibRef

Kortylewski, A., He, J., Liu, Q., Yuille, A.L.,
Compositional Convolutional Neural Networks: A Deep Architecture With Innate Robustness to Partial Occlusion,
CVPR20(8937-8946)
IEEE DOI 2008
Robustness, Training, Computational modeling, Solid modeling, Computer architecture, Artificial neural networks BibRef

Yuille, A.L.[Alan L.], Liu, C.X.[Chen-Xi],
Deep Nets: What have They Ever Done for Vision?,
IJCV(129), No. 3, March 2021, pp. 781-802.
Springer DOI 2103
We argue that Deep Nets in their current form are unlikely to be able to overcome the fundamental problem of computer vision, namely how to deal with the combinatorial explosion, caused by the enormous complexity of natural images, and obtain the rich understanding of visual scenes that the human visual achieves. BibRef

Balestriero, R.[Randall], Baraniuk, R.G.[Richard G.],
Mad Max: Affine Spline Insights Into Deep Learning,
PIEEE(109), No. 5, May 2021, pp. 704-727.
IEEE DOI 2105
Splines (mathematics), Standards, Deep learning, Convolution, Task analysis, Recurrent neural networks, Quantization (signal), Voronoi diagram BibRef

Liu, D.[Defu], Ning, J.[Jin], Wu, J.Z.[Jin-Zhao], Yang, G.W.[Guo-Wu],
Extending Ordinary-Label Learning Losses to Complementary-Label Learning,
SPLetters(28), 2021, pp. 852-856.
IEEE DOI 2106
Training, Neural networks, Signal processing algorithms, Risk management, Mean square error methods, Machine learning, deep neural networks BibRef

Zeng, X.F.[Xian-Fang], Wu, W.X.[Wen-Xuan], Tian, G.Z.[Guang-Zhong], Li, F.X.[Fu-Xin], Liu, Y.[Yong],
Deep Superpixel Convolutional Network for Image Recognition,
SPLetters(28), 2021, pp. 922-926.
IEEE DOI 2106
Convolution, Task analysis, Standards, Image recognition, Kernel, Feature extraction, Deep learning, superpixel BibRef

Zhao, B.X.[Bao-Xin], Xiong, H.Y.[Hao-Yi], Bian, J.[Jiang], Guo, Z.S.[Zhi-Shan], Xu, C.Z.[Cheng-Zhong], Dou, D.[Dejing],
COMO: Efficient Deep Neural Networks Expansion With COnvolutional MaxOut,
MultMed(23), 2021, pp. 1722-1730.
IEEE DOI 2106
Convolution, Spatial resolution, Convolutional neural networks, Deep learning, Computer architecture, Transforms, neural networks BibRef

Wang, Q.L.[Qi-Long], Xie, J.T.[Jiang-Tao], Zuo, W.M.[Wang-Meng], Zhang, L.[Lei], Li, P.H.[Pei-Hua],
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization,
PAMI(43), No. 8, August 2021, pp. 2582-2597.
IEEE DOI 2107
Covariance matrices, Robustness, Estimation, Geometry, Measurement, Visualization, Complexity theory, Global covariance pooling, visual recognition BibRef

Chen, Z.[Zhe], Wu, X.J.[Xiao-Jun], Xu, T.Y.[Tian-Yang], Kittler, J.V.[Josef V.],
Learning Alternating Deep-Layer Cascaded Representation,
SPLetters(28), 2021, pp. 1520-1524.
IEEE DOI 2108
Training, Encoding, Collaboration, Mathematical model, Feature extraction, Deep learning, Computer architecture, image classification BibRef

Fan, B.[Bin], Liu, H.M.[Hong-Min], Zeng, H.[Hui], Zhang, J.Y.[Ji-Yong], Liu, X.[Xin], Han, J.W.[Jun-Wei],
Deep Unsupervised Binary Descriptor Learning Through Locality Consistency and Self Distinctiveness,
MultMed(23), 2021, pp. 2770-2781.
IEEE DOI 2109
Machine learning, Robustness, Quantization (signal), Binary codes, Task analysis, Feature extraction, Training, Unsupervised learning, image retrieval BibRef

Wang, X.[Xin], Wang, S.[Shiyi], Ning, C.[Chen], Zhou, H.Y.[Hui-Yu],
Enhanced Feature Pyramid Network With Deep Semantic Embedding for Remote Sensing Scene Classification,
GeoRS(59), No. 9, September 2021, pp. 7918-7932.
IEEE DOI 2109
Feature extraction, Semantics, Spatial resolution, Convolution, Remote sensing, Deconvolution, Task analysis, scene classification BibRef

Miao, Y.Q.[Yun-Qi], Lin, Z.J.[Zi-Jia], Ma, X.[Xiao], Ding, G.G.[Gui-Guang], Han, J.G.[Jun-Gong],
Learning Transformation-Invariant Local Descriptors With Low-Coupling Binary Codes,
IP(30), 2021, pp. 7554-7566.
IEEE DOI 2109
Correlation, Binary codes, Training, Visualization, Feature extraction, Entropy, Deep learning, deep learning BibRef

Yang, S.J.[Shi-Jie], Li, L.[Liang], Wang, S.H.[Shu-Hui], Zhang, W.G.[Wei-Gang], Huang, Q.M.[Qing-Ming], Tian, Q.[Qi],
Graph Regularized Encoder-Decoder Networks for Image Representation Learning,
MultMed(23), 2021, pp. 3124-3136.
IEEE DOI 2109
BibRef
Earlier: A1, A2, A3, A4, A5, Only:
A Graph Regularized Deep Neural Network for Unsupervised Image Representation Learning,
CVPR17(7053-7061)
IEEE DOI 1711
Laplace equations, Visualization, Manifolds, Image reconstruction, Task analysis, Decoding, Semantics, Auto-encoder, encoder-decoder, image representation learning. Neural networks, Robustness. BibRef

Zhao, T.Y.[Tian-Yu], Zhao, J.[Jian], Zhou, W.G.[Wen-Gang], Zhou, Y.[Yun], Li, H.Q.[Hou-Qiang],
State Representation Learning With Adjacent State Consistency Loss for Deep Reinforcement Learning,
MultMedMag(28), No. 3, July 2021, pp. 117-127.
IEEE DOI 2109
Training data, Task analysis, Reinforcement learning, Feature extraction, Games, Neural networks, Deep learning BibRef

Liu, R.[Rui], Liu, Y.[Yi], Wang, R.[Rui], Zhou, Y.C.[Yu-Cong],
Mutual calibration training: Training deep neural networks with noisy labels using dual-models,
CVIU(212), 2021, pp. 103277.
Elsevier DOI 2110
Image classification, Deep neural networks, Weak supervisor BibRef

Chen, Z.D.[Zhao-Dong], Deng, L.[Lei], Wang, B.Y.[Bang-Yan], Li, G.Q.[Guo-Qi], Xie, Y.[Yuan],
A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks,
PAMI(44), No. 1, January 2022, pp. 13-31.
IEEE DOI 2112
Jacobian matrices, Explosions, Measurement, Biological neural networks, Probability, Libraries, gradient norm equality BibRef

Sommer, S.[Stefan], Bronstein, A.M.[Alex M.],
Horizontal Flows and Manifold Stochastics in Geometric Deep Learning,
PAMI(44), No. 2, February 2022, pp. 811-822.
IEEE DOI 2201
Manifolds, Convolution, Machine learning, Geometry, Stochastic processes, Bridges, Neural networks, bridge sampling BibRef

Yan, M.[Ming], Yang, J.X.[Jian-Xi], Chen, C.[Cen], Zhou, J.T.[Joey Tianyi], Pan, Y.[Yi], Zeng, Z.[Zeng],
Enhanced gradient learning for deep neural networks,
IET-IPR(16), No. 2, 2022, pp. 365-377.
DOI Link 2201
BibRef

Li, Z.Y.[Zheng-Ying], Huang, H.[Hong], Zhang, Z.[Zhen], Shi, G.Y.[Guang-Yao],
Manifold-Based Multi-Deep Belief Network for Feature Extraction of Hyperspectral Image,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Zhu, L.[Lei], She, Q.[Qi], Li, D.[Duo], Lu, Y.[Yanye], Kang, X.J.[Xue-Jing], Hu, J.[Jie], Wang, C.H.[Chang-Hu],
Unifying Nonlocal Blocks for Neural Networks,
ICCV21(12272-12281)
IEEE DOI 2203
Deep learning, Image segmentation, Image recognition, Neural networks, Semantics, Information filters, Video analysis and understanding BibRef

Li, Y.[Yao], Min, M.R.[Martin Renqiang], Lee, T.[Thomas], Yu, W.C.[Wen-Chao], Kruus, E.[Erik], Wang, W.[Wei], Hsieh, C.J.[Cho-Jui],
Towards Robustness of Deep Neural Networks via Regularization,
ICCV21(7476-7485)
IEEE DOI 2203
Deep learning, Manifolds, Analytical models, Computational modeling, Neural networks, Benchmark testing, Recognition and classification BibRef

Vasconcelos, C.[Cristina], Larochelle, H.[Hugo], Dumoulin, V.[Vincent], Romijnders, R.[Rob], Roux, N.L.[Nicolas Le], Goroshin, R.[Ross],
Impact of Aliasing on Generalization in Deep Convolutional Networks,
ICCV21(10509-10518)
IEEE DOI 2203
Convolutional codes, Art, Convolution, Low-pass filters, Computer architecture, Performance gain, Representation learning, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Yuan, K.[Kun], Chen, Y.M.[Yi-Ming], Huang, X.[Xinmeng], Zhang, Y.[Yingya], Pan, P.[Pan], Xu, Y.H.[Ying-Hui], Yin, W.T.[Wo-Tao],
DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training,
ICCV21(3009-3019)
IEEE DOI 2203
Training, Degradation, Deep learning, Runtime, Computational modeling, Task analysis, Machine learning architectures and formulations BibRef

Huang, S.[Shihua], Lu, Z.C.[Zhi-Chao], Cheng, R.[Ran], He, C.[Cheng],
FaPN: Feature-aligned Pyramid Network for Dense Image Prediction,
ICCV21(844-853)
IEEE DOI 2203
Code, Deep Learning.
WWW Link. Deep learning, Image segmentation, Codes, Neural networks, Computer architecture, Feature extraction, grouping and shape BibRef

Ramé, A.[Alexandre], Sun, R.[Rémy], Cord, M.[Matthieu],
MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks,
ICCV21(803-813)
IEEE DOI 2203
Training, Fitting, Data models, Image classification, Recognition and classification, Optimization and learning methods BibRef

Tiwari, L.[Lokender], Madan, A.[Anish], Anand, S.[Saket], Banerjee, S.[Subhashis],
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions,
WACV22(3829-3838)
IEEE DOI 2202
Training, Deep learning, Perturbation methods, Neurons, Neural networks, Training data, Predictive models, Vision Systems and Applications Deep Learning BibRef

Boyd, A.[Aidan], Bowyer, K.[Kevin], Czajka, A.[Adam],
Human-Aided Saliency Maps Improve Generalization of Deep Learning,
WACV22(1255-1264)
IEEE DOI 2202
Deep learning, Training, Image coding, Biometrics (access control), Human intelligence, Security/Surveillance BibRef

Lengyel, A.[Attila], van Gemert, J.C.[Jan C.],
Exploiting Learned Symmetries in Group Equivariant Convolutions,
ICIP21(759-763)
IEEE DOI 2201
Convolutional codes, Deep learning, Image processing, Convolutional neural networks, group equivariant convolutions, efficient deep learning BibRef

Mdrafi, R.[Robiulhossain], Gurbuz, A.C.[Ali Cafer],
Compressed Classification from Learned Measurements,
LCI21(4021-4030)
IEEE DOI 2112
Classification form compressive sensed data. Weight measurement, Deep learning, Image coding, Loss measurement, Robustness BibRef

Fields, G.[Greg], Samragh, M.[Mohammad], Javaheripi, M.[Mojan], Koushanfar, F.[Farinaz], Javidi, T.[Tara],
Trojan Signatures in DNN Weights,
AROW21(12-20)
IEEE DOI 2112
Training, Deep learning, Computational modeling, Computer architecture, Trojan horses BibRef

Lebrat, L.[Léo], Cruz, R.S.[Rodrigo Santa], Fookes, C.[Clinton], Salvado, O.[Olivier],
MongeNet: Efficient Sampler for Geometric Deep Learning,
CVPR21(16659-16668)
IEEE DOI 2111
Training, Deep learning, Solid modeling, Computational modeling, Estimation, Approximation error BibRef

Durasov, N.[Nikita], Bagautdinov, T.[Timur], Baque, P.[Pierre], Fua, P.[Pascal],
Masksembles for Uncertainty Estimation,
CVPR21(13534-13543)
IEEE DOI 2111
Deep learning, Training, Uncertainty, Costs, Computational modeling, Estimation, Reinforcement learning BibRef

Huang, L.[Lei], Zhou, Y.[Yi], Liu, L.[Li], Zhu, F.[Fan], Shao, L.[Ling],
Group Whitening: Balancing Learning Efficiency and Representational Capacity,
CVPR21(9507-9516)
IEEE DOI 2111
Deep learning, Analytical models, Sociology, Computer architecture, Standardization, Benchmark testing BibRef

Wang, X.S.[Xin-Shao], Hua, Y.[Yang], Kodirov, E.[Elyor], Clifton, D.A.[David A.], Robertson, N.M.[Neil M.],
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks,
CVPR21(752-761)
IEEE DOI 2111
Training, Deep learning, Semantics, Neural networks, Predictive models, Minimization, Entropy BibRef

Xu, Y.J.[You-Jiang], Zhu, L.C.[Lin-Chao], Jiang, L.[Lu], Yang, Y.[Yi],
Faster Meta Update Strategy for Noise-Robust Deep Learning,
CVPR21(144-153)
IEEE DOI 2111
Training, Deep learning, Training data, Robustness, Pattern recognition, Noise robustness BibRef

Pestana, C.[Camilo], Liu, W.[Wei], Glance, D.[David], Owens, R.[Robyn], Mian, A.[Ajmal],
Assistive Signals for Deep Neural Network Classifiers,
LXCV21(1221-1225)
IEEE DOI 2109
Deep learning, Perturbation methods, Optimization methods, Lighting BibRef

Lin, Y.[Yi], Wang, N.[Namin], Ma, X.Q.[Xiao-Qing], Li, Z.[Ziwei], Bai, G.[Gang],
How Does DCNN Make Decisions ?,
ICPR21(3342-3349)
IEEE DOI 2105
Training, Convolution, Semantics, Decision making, Computer architecture, Visual systems, Feature extraction, Deep Convolutional Neural Network BibRef

Mastan, I.D.[Indra Deep], Raman, S.[Shanmuganathan],
DeepCFL: Deep Contextual Features Learning from a Single Image,
WACV21(2896-2905)
IEEE DOI 2106
Training, Image synthesis, Semantics, Training data, Image restoration BibRef

Kobayashi, T.[Takumi],
Phase-wise Parameter Aggregation For Improving SGD Optimization,
WACV21(2624-2633)
IEEE DOI 2106
Stochastic gradient descent. Applied to deep net training. Training, Optimization methods, Convolutional neural networks, Task analysis, Surface treatment BibRef

Ding, Y.F.[Yi-Fan], Wang, L.Q.[Li-Qiang], Gong, B.Q.[Bo-Qing],
Analyzing Deep Neural Network's Transferability via Fréchet Distance,
WACV21(3931-3940)
IEEE DOI 2106
Measurement, Degradation, Training, Correlation, Transfer learning, Neural networks BibRef

Jamadandi, A.[Adarsh], Tigadoli, R.[Rishabh], Tabib, R.[Ramesh], Mudenagudi, U.[Uma],
Probabilistic Word Embeddings in Kinematic Space,
ICPR21(8759-8765)
IEEE DOI 2105
Geometry, Uncertainty, Computational modeling, Kinematics, Transforms, Aerospace electronics, Tools BibRef

Ding, P.L.K.[Pak Lun Kevin], Martin, S.[Sarah], Li, B.X.[Bao-Xin],
Improving Batch Normalization with Skewness Reduction for Deep Neural Networks,
ICPR21(7165-7172)
IEEE DOI 2105
Training, Neural networks, Transforms, Optimization BibRef

Shiran, G.[Guy], Weinshall, D.[Daphna],
Multi-Modal Deep Clustering: Unsupervised Partitioning of Images,
ICPR21(4728-4735)
IEEE DOI 2105
Deep learning, Neural networks, Image representation, Benchmark testing, Task analysis, Gaussian mixture model BibRef

Liao, W.H.[Wen-Hung], Huang, Y.T.[Yen-Ting],
Investigation of DNN Model Robustness Using Heterogeneous Datasets,
ICPR21(4393-4397)
IEEE DOI 2105
Training, Deep learning, Image coding, Neural networks, Training data, Feature extraction, Data models BibRef

Nakada, M.[Masaki], Chen, H.[Honglin], Lakshmipathy, A.[Arjun], Terzopoulos, D.[Demetri],
Locally-Connected, Irregular Deep Neural Networks for Biomimetic Active Vision in a Simulated Human,
ICPR21(4465-4472)
IEEE DOI 2105
Visualization, Solid modeling, Biological system modeling, Training data, Prototypes, Retina BibRef

Berg, A.[Axel], Oskarsson, M.[Magnus], O'Connor, M.[Mark],
Deep Ordinal Regression with Label Diversity,
ICPR21(2740-2747)
IEEE DOI 2105
Training, Learning systems, Neural networks, Pose estimation, Diversity methods, Predictive models, Search problems BibRef

Takenaga, S.[Shintaro], Watanabe, S.[Shuhei], Nomura, M.[Masahiro], Ozaki, Y.[Yoshihiko], Onishi, M.[Masaki], Habe, H.[Hitoshi],
Evaluating Initialization of Nelder-Mead Method for Hyperparameter Optimization in Deep Learning,
ICPR21(3372-3379)
IEEE DOI 2105
Deep learning, Shape, Market research, Optimization BibRef

Georgiou, T.[Theodoros], Schmitt, S.[Sebastian], Bäck, T.[Thomas], Pu, N.[Nan], Chen, W.[Wei], Lew, M.[Michael],
Comparison of deep learning and hand crafted features for mining simulation data,
ICPR21(1-8)
IEEE DOI 2105
Deep learning, Solid modeling, Dictionaries, Computational modeling, Detectors BibRef

Hansen, P.[Patrick], Vilkin, A.[Alexey], Krustalev, Y.[Yury], Imber, J.[James], Talagala, D.[Dumidu], Hanwell, D.[David], Mattina, M.[Matthew], Whatmough, P.N.[Paul N.],
ISP4ML: The Role of Image Signal Processing in Efficient Deep Learning Vision Systems,
ICPR21(2438-2445)
IEEE DOI 2105
Training, Machine vision, Pipelines, Memory management, Signal processing, Market research, Software BibRef

Poyser, M.[Matt], Atapour-Abarghouei, A.[Amir], Breckon, T.P.[Toby P.],
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures,
ICPR21(2830-2837)
IEEE DOI 2105
Performance evaluation, Image segmentation, Image coding, Pose estimation, Transform coding, Network architecture, Video compression BibRef

Brigato, L.[Lorenzo], Barz, B.[Björn], Iocchi, L.[Luca], Denzler, J.[Joachim],
Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification,
VIPriors21(1071-1080)
IEEE DOI 2112
Deep learning, Training, Satellites, Head, Pipelines, Benchmark testing, Standards BibRef

Brigato, L.[Lorenzo], Iocchi, L.[Luca],
A Close Look at Deep Learning with Small Data,
ICPR21(2490-2497)
IEEE DOI 2105
Training, Deep learning, Computational modeling, Pipelines, Computer architecture, Benchmark testing, Complexity theory BibRef

Du, S.Y.[Shuai-Yuan], Hong, C.Y.[Chao-Yi], Pan, Z.Y.[Zhi-Yu], Feng, C.[Chen], Cao, Z.G.[Zhi-Guo],
Parallel Network to Learn Novelty from the Known,
ICPR21(2172-2179)
IEEE DOI 2105
Training, Aggregates, Training data, Benchmark testing, Data models, Anomaly detection BibRef

Meng, L.H.[Ling-Heng], Gorbet, R.[Rob], Kulic, D.[Dana],
The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning,
ICPR21(347-353)
IEEE DOI 2105
Computational modeling, Neural networks, Buildings, Reinforcement learning, Approximation error BibRef

Georgiou, T.[Theodoros], Schmitt, S.[Sebastian], Bäck, T.[Thomas], Chen, W.[Wei], Lew, M.[Michael],
Norm Loss: An efficient yet effective regularization method for deep neural networks,
ICPR21(8812-8818)
IEEE DOI 2105
Training, Manifolds, Neural networks, Computer architecture, Benchmark testing, Pattern recognition, Computational efficiency BibRef

Jie, R.L.[Ren-Long], Gao, J.B.[Jun-Bin], Vasnev, A.[Andrey], Tran, M.N.[Minh-Ngoc],
Regularized Flexible Activation Function Combination for Deep Neural Networks,
ICPR21(2001-2008)
IEEE DOI 2105
Convolutional codes, Image coding, Time series analysis, Neural networks, Stability criteria, Predictive models, Pattern recognition BibRef

Goncalves do Santos, C.F.[Claudio Filipi], Colombo, D.[Danilo], Roder, M.[Mateus], Papa, J.P.[João Paulo],
MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values,
ICPR21(2671-2676)
IEEE DOI 2105
Deep learning, Neurons, Turning, Pattern recognition, Convolutional neural networks, Biological neural networks, Image classification BibRef

Deng, X.[Xiang], Zhang, Z.F.M.[Zhong-Fei Mark],
Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?,
ICPR21(150-157)
IEEE DOI 2105
Training, Supervised learning, Neural networks, Benchmark testing, Linear programming, Pattern recognition, Computational efficiency BibRef

Beeching, E.[Edward], Debangoye, J.[Jilles], Simonin, O.[Oliver], Wolf, C.[Christian],
Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer,
ICPR21(158-165)
IEEE DOI 2105
Training, Solid modeling, Reinforcement learning, Benchmark testing, Cognition, Supercomputers BibRef

Rodríguez-Rodríguez, J.A.[José A.], Molina-Cabello, M.A.[Miguel A.], Benítez-Rochel, R.[Rafaela], López-Rubio, E.[Ezequiel],
The Effect of Noise and Brightness on Convolutional Deep Neural Networks,
MOI2QDN20(639-654).
Springer DOI 2103
BibRef

Li, X.L.[Xi-Lai], Sun, W.[Wei], Wu, T.F.[Tian-Fu],
Attentive Normalization,
ECCV20(XVII:70-87).
Springer DOI 2011
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Ma, X., Qiao, Z., Guo, J., Tang, S., Chen, Q., Yang, Q., Fu, S.,
Cascaded Context Dependency: An Extremely Lightweight Module For Deep Convolutional Neural Networks,
ICIP20(1741-1745)
IEEE DOI 2011
Feature extraction, Task analysis, Computer architecture, Object detection, Charge coupled devices, Training, multi-scale BibRef

Oyedotun, O.K.[Oyebade K.], Shabayek, A.E.[Abd El_Rahman], Aouada, D.[Djamila], Ottersten, B.[Björn],
Revisiting the Training of Very Deep Neural Networks without Skip Connections,
ICPR21(2724-2731)
IEEE DOI 2105
BibRef
Earlier:
Going Deeper With Neural Networks Without Skip Connections,
ICIP20(1756-1760)
IEEE DOI 2011
Training, Neural networks, Explosions, Pattern recognition, Task analysis, Optimization. Error analysis, Optimization, Computational modeling, Deep neural network, PlainNet, classification BibRef

You, J., Korhonen, J.,
Attention Boosted Deep Networks For Video Classification,
ICIP20(1761-1765)
IEEE DOI 2011
Feature extraction, Video sequences, Computer architecture, video classification BibRef

Zhang, X.[Xiao], Zhao, R.[Rui], Qiao, Y.[Yu], Li, H.S.[Hong-Sheng],
RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis Function Softmax,
ECCV20(XXVI:296-311).
Springer DOI 2011
Code, RBF.
WWW Link. 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 ReLU,
ECCV20(XIX:351-367).
Springer DOI 2011
Rectified linear units BibRef

Reimers, C.[Christian], Runge, J.[Jakob], Denzler, J.[Joachim],
Determining the Relevance of Features for Deep Neural Networks,
ECCV20(XXVI:330-346).
Springer DOI 2011
BibRef

Zhao, J.J.[Jun-Jie], Lu, D.H.[Dong-Huan], Ma, K.[Kai], Zhang, Y.[Yu], Zheng, Y.F.[Ye-Feng],
Deep Image Clustering with Category-style Representation,
ECCV20(XIV:54-70).
Springer DOI 2011

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Chen, D.D.[Dong-Dong], Davies, M.E.[Mike E.],
Deep Decomposition Learning for Inverse Imaging Problems,
ECCV20(XXVIII:510-526).
Springer DOI 2011
Code, DNN.
WWW Link. BibRef

Huang, L.[Lei], Qin, J.[Jie], Liu, L.[Li], Zhu, F.[Fan], Shao, L.[Ling],
Layer-wise Conditioning Analysis in Exploring the Learning Dynamics of DNNs,
ECCV20(II:384-401).
Springer DOI 2011
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Li, D.[Duo], Chen, Q.F.[Qi-Feng],
Deep Reinforced Attention Learning for Quality-Aware Visual Recognition,
ECCV20(XVI: 493-509).
Springer DOI 2010
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Yong, H.W.[Hong-Wei], Huang, J.Q.[Jian-Qiang], Meng, D.Y.[De-Yu], Hua, X.S.[Xian-Sheng], Zhang, L.[Lei],
Momentum Batch Normalization for Deep Learning with Small Batch Size,
ECCV20(XII: 224-240).
Springer DOI 2010
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Gustafsson, F.K., Danelljan, M., Schon, T.B.,
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision,
SAIAD20(1289-1298)
IEEE DOI 2008
Uncertainty, Task analysis, Estimation, Predictive models, Bayes methods, Machine learning BibRef

Dong, X.Y.[Xiao-Yi], Chen, D.D.[Dong-Dong], Zhou, H.[Hang], Hua, G.[Gang], Zhang, W.M.[Wei-Ming], Yu, N.H.[Neng-Hai],
Self-Robust 3D Point Recognition via Gather-Vector Guidance,
CVPR20(11513-11521)
IEEE DOI 2008
Robustness, Perturbation methods, Training, Image restoration, Aircraft BibRef

Rao, K.[Kanishka], Harris, C.[Chris], Irpan, A.[Alex], Levine, S.[Sergey], Ibarz, J.[Julian], Khansari, M.[Mohi],
RL-CycleGAN: Reinforcement Learning Aware Simulation-to-Real,
CVPR20(11154-11163)
IEEE DOI 2008
Task analysis, Robots, Adaptation models, Grasping, Training, Learning (artificial intelligence) BibRef

Shi, Y.[Yi], Xu, M.C.[Meng-Chen], Yuan, S.H.[Shuai-Hang], Fang, Y.[Yi],
Unsupervised Deep Shape Descriptor With Point Distribution Learning,
CVPR20(9350-9359)
IEEE DOI 2008
To learn shapes. Shape, Robustness, Decoding, Solid modeling, Training, Neural networks BibRef

Xu, Y.H.[Yuan-Hong], Qian, Q.[Qi], Li, H.[Hao], Jin, R.[Rong], Hu, J.[Juhua],
Weakly Supervised Representation Learning with Coarse Labels,
ICCV21(10573-10581)
IEEE DOI 2203
Deep learning, Training, Representation learning, Visualization, Benchmark testing, Data collection, Representation learning, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Qian, Q.[Qi], Hu, J.H.[Ju-Hua], Li, H.[Hao],
Hierarchically Robust Representation Learning,
CVPR20(7334-7342)
IEEE DOI 2008
Robustness, Task analysis, Feature extraction, Optimization, Benchmark testing, Training, Data models BibRef

Huang, L.[Lei], Liu, L.[Li], Zhu, F.[Fan], Wan, D.W.[Di-Wen], Yuan, Z.H.[Ze-Huan], Li, B.[Bo], Shao, L.[Ling],
Controllable Orthogonalization in Training DNNs,
CVPR20(6428-6437)
IEEE DOI 2008
Training, Matrix decomposition, Convergence, Neural networks, Eigenvalues and eigenfunctions, Covariance matrices, Jacobian matrices BibRef

Zhang, R., Peng, Z., Wu, L., Li, Z., Luo, P.,
Exemplar Normalization for Learning Deep Representation,
CVPR20(12723-12732)
IEEE DOI 2008
Task analysis, Training, Tensile stress, Standards, Switches, Noise measurement, Benchmark testing BibRef

Jaiswal, M.S., Kang, B., Lee, J., Cho, M.,
MUTE: Inter-class Ambiguity Driven Multi-hot Target Encoding for Deep Neural Network Design,
DeepVision20(3254-3263)
IEEE DOI 2008
Encoding, Neural networks, Hamming distance, Noise measurement, Semantics, Computational modeling, Training BibRef

Le, E., Kokkinos, I., Mitra, N.J.,
Going Deeper With Lean Point Networks,
CVPR20(9500-9509)
IEEE DOI 2008
Convolution, Memory management, Training BibRef

Zhuang, C., She, T., Andonian, A., Sobol Mark, M., Yamins, D.,
Unsupervised Learning From Video With Deep Neural Embeddings,
CVPR20(9560-9569)
IEEE DOI 2008
Task analysis, Visualization, Unsupervised learning, Neural networks, Computer architecture, Image recognition BibRef

Xie, X., Kim, K.,
Partial Weight Adaptation for Robust DNN Inference,
CVPR20(9570-9578)
IEEE DOI 2008
Distortion, Training, Streaming media, Robustness, Frequency response, Sensitivity, Training data BibRef

Li, P., Zhao, H., Liu, H.,
Deep Fair Clustering for Visual Learning,
CVPR20(9067-9076)
IEEE DOI 2008
Clustering algorithms, Visualization, Partitioning algorithms, Clustering methods, Machine learning, Training, Measurement BibRef

Mittal, G., Liu, C., Karianakis, N., Fragoso, V., Chen, M., Fu, Y.,
HyperSTAR: Task-Aware Hyperparameters for Deep Networks,
CVPR20(8733-8742)
IEEE DOI 2008
Task analysis, Acceleration, Training, Visualization, Optimization, Bayes methods, Gaussian processes BibRef

Huang, J., Gong, S., Zhu, X.,
Deep Semantic Clustering by Partition Confidence Maximisation,
CVPR20(8846-8855)
IEEE DOI 2008
Indexes, Training, Uncertainty, Semantics, Visualization, Clustering methods, Machine learning BibRef

Huang, Y., Yu, Y.,
An Internal Covariate Shift Bounding Algorithm for Deep Neural Networks by Unitizing Layers' Outputs,
CVPR20(8462-8470)
IEEE DOI 2008
Integrated circuits, Upper bound, Training, Neural networks, Convergence, Noise measurement, Earth BibRef

Joneidi, M., Vahidian, S., Esmaeili, A., Wang, W., Rahnavard, N., Lin, B., Shah, M.,
Select to Better Learn: Fast and Accurate Deep Learning Using Data Selection From Nonlinear Manifolds,
CVPR20(7816-7826)
IEEE DOI 2008
Manifolds, Training, Machine learning, Face, Time complexity BibRef

Chrysos, G.G.[Grigorios G.], Moschoglou, S.[Stylianos], Bouritsas, G.[Giorgos], Panagakis, Y.[Yannis], Deng, J.K.[Jian-Kang], Zafeiriou, S.P.[Stefanos P.],
P-nets: Deep Polynomial Neural Networks,
CVPR20(7323-7333)
IEEE DOI 2008
Neural networks, Tensile stress, Task analysis, Training, Image generation BibRef

Zhang, X., Qin, S., Xu, Y., Xu, H.,
Quaternion Product Units for Deep Learning on 3D Rotation Groups,
CVPR20(7302-7311)
IEEE DOI 2008
Quaternions, Robustness, Skeleton, Algebra, Data models, Machine learning BibRef

Wang, Z., Hu, G., Hu, Q.,
Training Noise-Robust Deep Neural Networks via Meta-Learning,
CVPR20(4523-4532)
IEEE DOI 2008
Noise measurement, Optimization, Training, Noise robustness, Natural language processing, Robustness BibRef

Meng, F., Cheng, H., Li, K., Xu, Z., Ji, R., Sun, X., Lu, G.,
Filter Grafting for Deep Neural Networks,
CVPR20(6598-6606)
IEEE DOI 2008
Training, Information filters, Filtering algorithms, Filtering theory, Entropy, Nickel BibRef

Zhan, X., Xie, J., Liu, Z., Ong, Y., Loy, C.C.,
Online Deep Clustering for Unsupervised Representation Learning,
CVPR20(6687-6696)
IEEE DOI 2008
Training, Feature extraction, Task analysis, Frequency modulation, Learning systems, Visualization, Clustering algorithms BibRef

Zhang, L., Qi, G.,
WCP: Worst-Case Perturbations for Semi-Supervised Deep Learning,
CVPR20(3911-3920)
IEEE DOI 2008
Perturbation methods, Training, Additives, Robustness, Predictive models, Data models, Additive noise BibRef

Song, J., Chen, Y., Ye, J., Wang, X., Shen, C., Mao, F., Song, M.,
DEPARA: Deep Attribution Graph for Deep Knowledge Transferability,
CVPR20(3921-3929)
IEEE DOI 2008
Task analysis, Computational modeling, Feature extraction, Data models, Dictionaries, Probes, Computer architecture BibRef

Liu, J., Sun, Y., Han, C., Dou, Z., Li, W.,
Deep Representation Learning on Long-Tailed Data: A Learnable Embedding Augmentation Perspective,
CVPR20(2967-2976)
IEEE DOI 2008
Training, Task analysis, Head, Distortion, Face recognition, Visualization, Additives BibRef

Wu, R.[Rundi], Zhuang, Y.X.[Yi-Xin], Xu, K.[Kai], Zhang, H.[Hao], Chen, B.Q.[Bao-Quan],
PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes,
CVPR20(826-835)
IEEE DOI 2008
Shape, Geometry, Solid modeling, Decoding, Adaptation models, Neural networks BibRef

Chen, H., Wang, Y., Xu, C., Shi, B., Xu, C., Tian, Q., Xu, C.,
AdderNet: Do We Really Need Multiplications in Deep Learning?,
CVPR20(1465-1474)
IEEE DOI 2008
Convolutional codes, Adders, Measurement, Computational complexity, Biological neural networks, Training BibRef

Lee, E., Lee, C.,
NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks,
CVPR20(1475-1484)
IEEE DOI 2008
Neurons, Computer architecture, Biological neural networks, Iterative methods, Redundancy, Taylor series, Computational efficiency BibRef

Gao, S., Huang, F., Pei, J., Huang, H.,
Discrete Model Compression With Resource Constraint for Deep Neural Networks,
CVPR20(1896-1905)
IEEE DOI 2008
Logic gates, Computational modeling, Stochastic processes, Neural networks, Training, Computational efficiency, Acceleration BibRef

Xu, A., Huo, Z., Huang, H.,
On the Acceleration of Deep Learning Model Parallelism With Staleness,
CVPR20(2085-2094)
IEEE DOI 2008
Training, Parallel processing, Computational modeling, Neural networks, Stochastic processes, Acceleration, Convergence BibRef

Nan, Y., Ji, H.,
Deep Learning for Handling Kernel/model Uncertainty in Image Deconvolution,
CVPR20(2385-2394)
IEEE DOI 2008
Kernel, Deconvolution, Image restoration, Convolution, Artificial neural networks, Robustness, Optimization BibRef

Singh, P., Varshney, M., Namboodiri, V.P.,
Cooperative Initialization based Deep Neural Network Training,
WACV20(1130-1139)
IEEE DOI 2006
Training, Neurons, Standards, Task analysis, Color, Neural networks, Computer science BibRef

Lo, E., Kohl, J.,
Internet of Things (IoT) Discovery Using Deep Neural Networks,
WACV20(795-803)
IEEE DOI 2006
Object detection, Training, Computer architecture, Internet of Things, Modulation, Spectrogram, Microprocessors BibRef

Han, J., Luo, P., Wang, X.,
Deep Self-Learning From Noisy Labels,
ICCV19(5137-5146)
IEEE DOI 2004
data handling, learning (artificial intelligence), neural nets, robust network, noisy labels, clean data, Optimization BibRef

Gowal, S.[Sven], Dvijotham, K.[Krishnamurthy], Stanforth, R.[Robert], Bunel, R.[Rudy], Qin, C.[Chongli], Uesato, J.[Jonathan], Arandjelovic, R.[Relja], Mann, T.A.[Timothy Arthur], Kohli, P.[Pushmeet],
Scalable Verified Training for Provably Robust Image Classification,
ICCV19(4841-4850)
IEEE DOI 2004
Robustness, Training, Neural networks, Perturbation methods, Upper bound, Adaptation models, Optimization BibRef

Singh, S.[Saurabh], Shrivastava, A.[Abhinav],
EvalNorm: Estimating Batch Normalization Statistics for Evaluation,
ICCV19(3632-3640)
IEEE DOI 2004
learning (artificial intelligence), object detection, batch normalization statistics, deep learning, peculiar behavior, Google BibRef

Huang, S.Y.[Shuai-Yi], Wang, Q.Y.[Qiu-Yue], Zhang, S.Y.[Song-Yang], Yan, S.P.[Shi-Peng], He, X.M.[Xu-Ming],
Dynamic Context Correspondence Network for Semantic Alignment,
ICCV19(2010-2019)
IEEE DOI 2004
image fusion, image representation, Pattern matching, supervised learning, dynamic context correspondence network. BibRef

Wang, Y.[Yisen], Ma, X.J.[Xing-Jun], Chen, Z.Y.[Zai-Yi], Luo, Y.[Yuan], Yi, J.F.[Jin-Feng], Bailey, J.[James],
Symmetric Cross Entropy for Robust Learning With Noisy Labels,
ICCV19(322-330)
IEEE DOI 2004
entropy, neural nets, symmetric cross entropy learning, noise robust counterpart reverse cross entropy, noisy labels, Task analysis BibRef

Maximov, M., Ritschel, T., Leal-Taixé, L., Fritz, M.,
Deep Appearance Maps,
ICCV19(8728-8737)
IEEE DOI 2004
gradient methods, image reconstruction, image representation, image segmentation, learning (artificial intelligence), lighting, Image color analysis BibRef

Wu, J., Long, K., Wang, F., Qian, C., Li, C., Lin, Z., Zha, H.,
Deep Comprehensive Correlation Mining for Image Clustering,
ICCV19(8149-8158)
IEEE DOI 2004
data mining, feature extraction, image representation, pattern clustering, unsupervised learning, Task analysis BibRef

Li, G., Müller, M., Thabet, A., Ghanem, B.,
DeepGCNs: Can GCNs Go As Deep As CNNs?,
ICCV19(9266-9275)
IEEE DOI 2004
convolutional neural nets, graph theory, image segmentation, learning (artificial intelligence), solid modelling, Stacking BibRef

Fong, R., Patrick, M., Vedaldi, A.,
Understanding Deep Networks via Extremal Perturbations and Smooth Masks,
ICCV19(2950-2958)
IEEE DOI 2004
image representation, neural nets, optimisation, smoothing methods, smooth masks, deep neural network, optimization problem BibRef

Pan, X., Zhan, X., Shi, J., Tang, X., Luo, P.,
Switchable Whitening for Deep Representation Learning,
ICCV19(1863-1871)
IEEE DOI 2004
convolutional neural nets, image representation, image segmentation, learning (artificial intelligence), Semantics BibRef

Li, H., Zhang, H., Qi, X., Ruigang, Y., Huang, G.,
Improved Techniques for Training Adaptive Deep Networks,
ICCV19(1891-1900)
IEEE DOI 2004
gradient methods, graph theory, groupware, image classification, inference mechanisms, learning (artificial intelligence), Knowledge transfer BibRef

Hernández-Garcia, A., König, P.,
Learning Representational Invariance Instead of Categorization,
Preregister19(4587-4590)
IEEE DOI 2004
image classification, learning (artificial intelligence), neural nets, object recognition, adversarial vulnerability, deep learning BibRef

Wang, C.Y.[Chien-Yao], Liao, H.Y.M.[Hong-Yuan Mark], Chen, P.Y.[Ping-Yang], Hsieh, J.W.[Jun-Wei],
Enriching Variety of Layer-Wise Learning Information by Gradient Combination,
LPCV19(2477-2484)
IEEE DOI 2004
feature extraction, image recognition, image segmentation, learning (artificial intelligence), object detection, object detection BibRef

Chen, H., Lin, M., Sun, X., Qi, Q., Li, H., Jin, R.,
MuffNet: Multi-Layer Feature Federation for Mobile Deep Learning,
CEFRL19(2943-2952)
IEEE DOI 2004
convolutional neural nets, image classification, image representation, learning (artificial intelligence), convolution network BibRef

Choi, J., Seo, H., Im, S., Kang, M.,
Attention Routing Between Capsules,
NeruArch19(1981-1989)
IEEE DOI 2004
affine transforms, feature extraction, image classification, learning (artificial intelligence), multilayer perceptrons, Deep learning BibRef

Durand, T.[Thibaut], Mehrasa, N.[Nazanin], Mori, G.[Greg],
Learning a Deep ConvNet for Multi-Label Classification With Partial Labels,
CVPR19(647-657).
IEEE DOI 2002
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Hossain, M.T.[Md Tahmid], Teng, S.W.[Shyh Wei], Zhang, D.S.[Deng-Sheng], Lim, S.[Suryani], Lu, G.J.[Guo-Jun],
Distortion Robust Image Classification Using Deep Convolutional Neural Network with Discrete Cosine Transform,
ICIP19(659-663)
IEEE DOI 1910
CNN, DCT, Dropout, Distortion, VGG16 BibRef

Arroyo, R.[Roberto], Tovar, J.[Javier], Delgado, F.J.[Francisco J.], Almazán, E.J.[Emilio J.], Serrador, D.G.[Diego G.], Hurtado, A.[Antonio],
Deep Learning of Visual and Textual Data for Region Detection Applied to Item Coding,
IbPRIA19(I:329-341).
Springer DOI 1910
Using text on the image. BibRef

Onchis, D.M.[Darian M.], Istin, C.[Codruta], Real, P.[Pedro],
Refined Deep Learning for Digital Objects Recognition via Betti Invariants,
CAIP19(I:613-621).
Springer DOI 1909
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Lan, X.[Xu], Zhu, X.T.[Xia-Tian], Gong, S.G.[Shao-Gang],
Self-Referenced Deep Learning,
ACCV18(II:284-300).
Springer DOI 1906
BibRef

Pai, G., Talmon, R., Bronstein, A., Kimmel, R.,
DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling,
WACV19(819-828)
IEEE DOI 1904
computational geometry, differential geometry, neural nets, sampling methods, unsupervised learning, DIMAL, Interpolation BibRef

Hinterstoisser, S.[Stefan], Lepetit, V.[Vincent], Wohlhart, P.[Paul], Konolige, K.[Kurt],
On Pre-trained Image Features and Synthetic Images for Deep Learning,
4DPose18(I:682-697).
Springer DOI 1905
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Haeusser, P.[Philip], Plapp, J.[Johannes], Golkov, V.[Vladimir], Aljalbout, E.[Elie], Cremers, D.[Daniel],
Associative Deep Clustering: Training a Classification Network with No Labels,
GCPR18(18-32).
Springer DOI 1905
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Zhang, H.[Huan], Shi, H.[Hong], Wang, W.W.[Wen-Wu],
Cascade Deep Networks for Sparse Linear Inverse Problems,
ICPR18(812-817)
IEEE DOI 1812
Inverse problems, Linear programming, Image resolution, Signal resolution, Convergence, Time complexity BibRef

Duan, Y.Q.[Yue-Qi], Wang, Z.W.[Zi-Wei], Lu, J.W.[Ji-Wen], Lin, X.D.[Xu-Dong], Zhou, J.[Jie],
GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning,
CVPR18(8270-8279)
IEEE DOI 1812
Reliability, Binary codes, Linear programming, Training, Mutual information BibRef

Keller, M.[Michel], Chen, Z.[Zetao], Maffra, F.[Fabiola], Schmuck, P.[Patrik], Chli, M.[Margarita],
Learning Deep Descriptors with Scale-Aware Triplet Networks,
CVPR18(2762-2770)
IEEE DOI 1812
Training, Neural networks, Task analysis, Feature extraction BibRef

Goh, C.K., Liu, Y., Kong, A.W.K.,
A Constrained Deep Neural Network for Ordinal Regression,
CVPR18(831-839)
IEEE DOI 1812
Optimization, Feature extraction, Neural networks, Support vector machines, Training, Measurement BibRef

Yang, Y.Q.[Yao-Qing], Feng, C.[Chen], Shen, Y.[Yiru], Tian, D.[Dong],
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation,
CVPR18(206-215)
IEEE DOI 1812
Decoding, Image reconstruction, Surface reconstruction, Neural networks BibRef

Caron, M.[Mathilde], Bojanowski, P.[Piotr], Joulin, A.[Armand], Douze, M.[Matthijs],
Deep Clustering for Unsupervised Learning of Visual Features,
ECCV18(XIV: 139-156).
Springer DOI 1810
BibRef

Xu, Y.F.[Yi-Fan], Fan, T.Q.[Tian-Qi], Xu, M.Y.[Ming-Ye], Zeng, L.[Long], Qiao, Y.[Yu],
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters,
ECCV18(VIII: 90-105).
Springer DOI 1810
BibRef

Wang, Q.[Qiang], Xu, J.Q.[Jia-Qing], Li, R.C.[Rong-Chun], Qiao, P.[Peng], Yang, K.[Ke], Li, S.J.[Shi-Jie], Dou, Y.[Yong],
Deep Image Clustering Using Convolutional Autoencoder Embedding with Inception-Like Block,
ICIP18(2356-2360)
IEEE DOI 1809
Convolutional codes, Convolution, Clustering algorithms, Image reconstruction, Decoding, Clustering methods, Task analysis, Kullback-Leibler divergence BibRef

Alqahtani, A., Xie, X., Deng, J., Jones, M.W.,
Learning Discriminatory Deep Clustering Models,
CAIP19(I:224-233).
Springer DOI 1909
BibRef
And:
A Deep Convolutional Auto-Encoder with Embedded Clustering,
ICIP18(4058-4062)
IEEE DOI 1809
Image reconstruction, Training, Feature extraction, Linear programming, Task analysis, Convolution, Cost function, Embedded Clustering BibRef

Chun, Y., Fessler, J.A.,
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery,
IVMSP18(1-5)
IEEE DOI 1809
Training, Convolution, Iterative decoding, Thresholding (Imaging), Imaging, Decoding, Encoding BibRef

Smith, K.E.[Kaleb E.], Williams, P.[Phillip], Chaiya, T.[Tatsanee], Ble, M.[Max],
Deep Convolutional-Shepard Interpolation Neural Networks for Image Classification Tasks,
ICIAR18(185-192).
Springer DOI 1807
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Wu, J., Qiu, S., Kong, Y., Chen, Y., Senhadji, L., Shu, H.,
MomentsNet: A simple learning-free method for binary image recognition,
ICIP17(2667-2671)
IEEE DOI 1803
Backpropagation, Feature extraction, Histograms, Image recognition, Machine learning, Transforms, Deep learning, MomentsNet, convolutional neural network BibRef

Nazaré, T.S.[Tiago S.], da Costa, G.B.P.[Gabriel B. Paranhos], Contato, W.A.[Welinton A.], Ponti, M.P.[Moacir P.],
Deep Convolutional Neural Networks and Noisy Images,
CIARP17(416-424).
Springer DOI 1802
BibRef

Dizaji, K.G.[Kamran Ghasedi], Herandi, A.[Amirhossein], Deng, C.[Cheng], Cai, W.D.[Wei-Dong], Huang, H.[Heng],
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization,
ICCV17(5747-5756)
IEEE DOI 1802
data visualisation, entropy, estimation theory, learning (artificial intelligence), minimisation, Tuning BibRef

Huang, L., Liu, X., Liu, Y., Lang, B., Tao, D.,
Centered Weight Normalization in Accelerating Training of Deep Neural Networks,
ICCV17(2822-2830)
IEEE DOI 1802
learning (artificial intelligence), multilayer perceptrons, neural nets, centered weight normalization, Training BibRef

Sun, C.[Chen], Shrivastava, A.[Abhinav], Singh, S.[Saurabh], Gupta, A.[Abhinav],
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era,
ICCV17(843-852)
IEEE DOI 1802
With a very large dataset. learning (artificial intelligence), pose estimation, JFT-300M dataset, base model, dataset size, Visualization BibRef

Park, E.[Eunhyeok], Ahn, J.[Junwhan], Yoo, S.[Sungjoo],
Weighted-Entropy-Based Quantization for Deep Neural Networks,
CVPR17(7197-7205)
IEEE DOI 1711
Computational modeling, Embedded systems, Entropy, Hardware, Mobile communication, Neural networks, Quantization, (signal) BibRef

Worrall, D.E.[Daniel E.], Garbin, S.J.[Stephan J.], Turmukhambetov, D.[Daniyar], Brostow, G.J.[Gabriel J.],
Harmonic Networks: Deep Translation and Rotation Equivariance,
CVPR17(7168-7177)
IEEE DOI 1711
To deal with rotations. Detectors, Filtering theory, Harmonic analysis, Maximum likelihood detection, Nonlinear filters, Power, harmonic, filters BibRef

Yang, X., Ramesh, P., Chitta, R., Madhvanath, S., Bernal, E.A., Luo, J.,
Deep Multimodal Representation Learning from Temporal Data,
CVPR17(5066-5074)
IEEE DOI 1711
Correlation, Data models, Decoding, Fuses, Machine learning, Robustness BibRef

Guo, Y.[Yiwen], Yao, A.B.[An-Bang], Zhao, H.[Hao], Chen, Y.R.[Yu-Rong],
Network Sketching: Exploiting Binary Structure in Deep CNNs,
CVPR17(4040-4048)
IEEE DOI 1711
Approximation algorithms, Computational modeling, Mathematical model, Memory management, Tensile, stress BibRef

Qiu, Z., Yao, T., Mei, T.,
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model,
CVPR17(4085-4094)
IEEE DOI 1711
Computational modeling, Computer architecture, Encoding, Optimization, Quantization (signal), Training, Visualization BibRef

Diba, A.[Ali], Sharma, V.[Vivek], Pazandeh, A., Pirsiavash, H., Van Gool, L.J.[Luc J.],
Weakly Supervised Cascaded Convolutional Networks,
CVPR17(5131-5139)
IEEE DOI 1711
Computer architecture, Feature extraction, Object detection, Proposals, Reliability, Training BibRef

Diba, A.[Ali], Sharma, V.[Vivek], Van Gool, L.J.[Luc J.],
Deep Temporal Linear Encoding Networks,
CVPR17(1541-1550)
IEEE DOI 1711
Computational modeling, Encoding, Optical fiber networks, Optical imaging, Robustness, Videos BibRef

Achsas, S., Nfaoui, E.H.,
Improving relational aggregated search from big data sources using deep learning,
ISCV17(1-6)
IEEE DOI 1710
Data mining, Feature extraction, Information retrieval, Neural networks, Big Data Sources, Deep Learning, Information Extraction, Information nuggets, Knowledge bases, Relational Aggregated Search, Stacked, Autoencoders BibRef

Dupre, R., Tzimiropoulos, G., Argyriou, V.,
Automated Risk Assessment for Scene Understanding and Domestic Robots Using RGB-D Data and 2.5D CNNs at a Patch Level,
DeepLearnRV17(476-477)
IEEE DOI 1709
Labeling, Machine learning, Shape, BibRef

Bentes Gatto, B.[Bernardo], de Souza, L.S.[Lincon Sales], dos Santos, E.M.[Eulanda M.],
A deep network model based on subspaces: A novel approach for image classification,
MVA17(436-439)
DOI Link 1708
Computer architecture, Discrete cosine transforms, Face, Face recognition, Machine learning, Neural networks, Principal component analysis BibRef

Mojoo, J.[Jonathan], Kurosawa, K.[Keiichi], Kurita, T.[Takio],
Deep CNN with Graph Laplacian Regularization for Multi-label Image Annotation,
ICIAR17(19-26).
Springer DOI 1706
BibRef

McCane, B., Szymanskic, L.,
Deep networks are efficient for circular manifolds,
ICPR16(3464-3469)
IEEE DOI 1705
Geometry, Logic gates, Manifolds, Neural networks, Neurons, Pattern recognition. BibRef

Zhao, Z.B.[Zhen-Bing], Xu, G.Z.[Guo-Zhi], Qi, Y.C.[Yin-Cheng],
Multi-Scale Hierarchy Deep Feature Aggregation for Compact Image Representations,
DeepVisual16(III: 557-571).
Springer DOI 1704
BibRef

Krutsch, R., Naidu, S.,
Monte Carlo method based precision analysis of deep convolution nets,
DASIP16(162-167)
IEEE DOI 1704
Monte Carlo methods BibRef

Cruz, R.S., Fernando, B.[Basura], Cherian, A., Gould, S.[Stephen],
DeepPermNet: Visual Permutation Learning,
CVPR17(6044-6052)
IEEE DOI 1711
Computational modeling, Image sequences, Machine learning, Training, Visualization BibRef

Yu, T.Y.[Tian-Yuan], Bai, L.[Liang], Guo, J.L.[Jin-Lin], Yang, Z.[Zheng], Xie, Y.X.[Yu-Xiang],
Deep Convolutional Neural Network for Bidirectional Image-Sentence Mapping,
MMMod17(II: 136-147).
Springer DOI 1701
BibRef

Islam, M.A.[M. Amirul], Rochan, M., Bruce, N.D.B.[Neil D.B.], Wang, Y.[Yang],
Gated Feedback Refinement Network for Dense Image Labeling,
CVPR17(4877-4885)
IEEE DOI 1711
BibRef
Earlier: A1, A3, A4, Only:
Dense Image Labeling Using Deep Convolutional Neural Networks,
CRV16(16-23)
IEEE DOI 1612
Convolution, Decoding, Encoding, Labeling, Logic gates, Semantics, Spatial resolution. Deep Convolutional Neural Network BibRef

Zheng, S., Song, Y., Leung, T., Goodfellow, I.J.[Ian J.],
Improving the Robustness of Deep Neural Networks via Stability Training,
CVPR16(4480-4488)
IEEE DOI 1612
BibRef

Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.B.,
Learning Deep Features for Discriminative Localization,
CVPR16(2921-2929)
IEEE DOI 1612
BibRef

Iandola, F.N., Moskewicz, M.W., Ashraf, K., Keutzer, K.,
FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters,
CVPR16(2592-2600)
IEEE DOI 1612
BibRef

Murdock, C., Li, Z., Zhou, H., Duerig, T.,
Blockout: Dynamic Model Selection for Hierarchical Deep Networks,
CVPR16(2583-2591)
IEEE DOI 1612
BibRef

Kalantidis, Y.[Yannis], Mellina, C.[Clayton], Osindero, S.[Simon],
Cross-Dimensional Weighting for Aggregated Deep Convolutional Features,
WebScale16(I: 685-701).
Springer DOI 1611
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Papadopoulos, G.T.[Georgios T.], Machairidou, E.[Elpida], Daras, P.[Petros],
Deep cross-layer activation features for visual recognition,
ICIP16(923-927)
IEEE DOI 1610
Correlation. Last layer of the CNN may not capture every scale of feature. BibRef

Qi, M.S.[Meng-Shi], Wang, Y.H.[Yun-Hong],
DEEP-CSSR: Scene classification using category-specific salient region with deep features,
ICIP16(1047-1051)
IEEE DOI 1610
Bio inspired models. BibRef

Anantrasirichai, N., Gilchrist, I.D., Bull, D.R.,
Visual salience and priority estimation for locomotion using a deep convolutional neural network,
ICIP16(1599-1603)
IEEE DOI 1610
Estimation BibRef

Blot, M., Robert, T., Thome, N., Cord, M.,
Shade: Information-Based Regularization for Deep Learning,
ICIP18(813-817)
IEEE DOI 1809
Entropy, Training, Task analysis, Machine learning, Neurons, Standards, Optimization, Deep learning, regularization, invariance, image understanding BibRef

Chaabouni, S., Benois-Pineau, J., Ben Amar, C.,
Transfer learning with deep networks for saliency prediction in natural video,
ICIP16(1604-1608)
IEEE DOI 1610
Benchmark testing BibRef

Makantasis, K., Doulamis, A., Doulamis, N., Psychas, K.,
Deep learning based human behavior recognition in industrial workflows,
ICIP16(1609-1613)
IEEE DOI 1610
Computer architecture BibRef

Gaur, U., Kourakis, M., Newman-Smith, E., Smith, W., Manjunath, B.S.,
Membrane segmentation via active learning with deep networks,
ICIP16(1943-1947)
IEEE DOI 1610
Computer architecture BibRef

Liu, M.Y.[Ming-Yu], Mallya, A.[Arun], Tuzel, O.[Oncel], Chen, X.[Xi],
Unsupervised network pretraining via encoding human design,
WACV16(1-9)
IEEE DOI 1606
Computer architecture. Deep NN training. BibRef

Porter, R.B., Zimmer, B.G.,
Deep segmentation networks using 'simple' multi-layered graphical models,
Southwest16(41-44)
IEEE DOI 1605
Feeds BibRef

Hiranandani, G., Karnick, H.,
Improved Classification and Reconstruction by Introducing Independence and Randomization in Deep Neural Networks,
DICTA15(1-8)
IEEE DOI 1603
image classification BibRef

Ueki, K., Kobayashi, T.,
Multi-layer feature extractions for image classification: Knowledge from deep CNNs,
WSSIP15(9-12)
IEEE DOI 1603
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And: WSSIP15(9-12)
IEEE DOI 1603
feature extraction. Computer vision 2 papers listed. BibRef

Ba, J.L.[Jimmy Lei], Swersky, K.[Kevin], Fidler, S.[Sanja], Salakhutdinov, R.[Ruslan],
Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions,
ICCV15(4247-4255)
IEEE DOI 1602
Electronic publishing BibRef

Huang, J.J.[Jia-Ji], Qiu, Q.[Qiang], Calderbank, R.[Robert], Sapiro, G.[Guillermo],
Geometry-Aware Deep Transform,
ICCV15(4139-4147)
IEEE DOI 1602
Machine learning. Use geometry. BibRef

Aubry, M.[Mathieu], Russell, B.C.[Bryan C.],
Understanding Deep Features with Computer-Generated Imagery,
ICCV15(2875-2883)
IEEE DOI 1602
Computational modeling BibRef

Cheng, Y., Yu, F.X., Feris, R.S., Kumar, S., Choudhary, A., Chang, S.F.,
An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections,
ICCV15(2857-2865)
IEEE DOI 1602
Complexity theory BibRef

Feng, J., Darrell, T.J.,
Learning the Structure of Deep Convolutional Networks,
ICCV15(2749-2757)
IEEE DOI 1602
Adaptation models BibRef

Wu, R., Wang, B., Wang, W., Yu, Y.,
Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification,
ICCV15(1287-1295)
IEEE DOI 1602
Aggregates BibRef

Yan, Z., Zhang, H., Piramuthu, R., Jagadeesh, V., de Coste, D., Di, W., Yu, Y.,
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition,
ICCV15(2740-2748)
IEEE DOI 1602
Computer architecture BibRef

Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., Moreno-Noguer, F.,
Discriminative Learning of Deep Convolutional Feature Point Descriptors,
ICCV15(118-126)
IEEE DOI 1602
Computational modeling BibRef

Monti, F., Boscaini, D., Masci, J., Rodolà, E., Svoboda, J., Bronstein, M.M.[Michael M.],
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs,
CVPR17(5425-5434)
IEEE DOI 1711
Computational modeling, Convolution, Laplace equations, Machine learning, Manifolds, Shape, BibRef

Gordo, A.[Albert], Gaidon, A.[Adrien], Perronnin, F.[Florent],
Deep Fishing: Gradient Features from Deep Nets,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Thewlis, J.[James], Zheng, S.[Shuai], Torr, P.H.S.[Philip H.S.], Vedaldi, A.[Andrea],
Fully-trainable deep matching,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Bilen, H., Vedaldi, A.,
Weakly Supervised Deep Detection Networks,
CVPR16(2846-2854)
IEEE DOI 1612
BibRef

Nguyen, K.[Kien], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Deep Context Modeling for Semantic Segmentation,
WACV17(56-63)
IEEE DOI 1609
BibRef
Earlier:
Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling,
ICIP16(1344-1348)
IEEE DOI 1610
BibRef
Earlier:
Improving deep convolutional neural networks with unsupervised feature learning,
ICIP15(2270-2274)
IEEE DOI 1512
Feature extraction, Graphical models, Image segmentation, Kernel, Labeling, Neural networks, Semantics, context modeling, scene parsing, scene understanding, semantic segmentation. Computational modeling. Convolutional Neural Network BibRef

Talathi, S.S.[Sachin S.],
Hyper-parameter optimization of deep convolutional networks for object recognition,
ICIP15(3982-3986)
IEEE DOI 1512
deep convolution networks BibRef

Yamashita, T.[Takayoshi], Tanaka, M.[Masayuki], Yamauchi, Y.[Yuji], Fujiyoshi, H.[Hironobu],
SWAP-NODE: A regularization approach for deep convolutional neural networks,
ICIP15(2475-2479)
IEEE DOI 1512
deep learning; dropout; regularization; swap-node BibRef

Afzal, M.Z.[Muhammad Zeshan], Capobianco, S.[Samuele], Malik, M.I.[Muhammad Imran], Marinai, S.[Simone], Breuel, T.M.[Thomas M.], Dengel, A.[Andreas], Liwicki, M.[Marcus],
Deepdocclassifier: Document classification with deep Convolutional Neural Network,
ICDAR15(1111-1115)
IEEE DOI 1511
Convolutional Neural Network;Deep CNN;Document Image Classification BibRef

Christodoulidis, S.[Stergios], Anthimopoulos, M.[Marios], Mougiakakou, S.[Stavroula],
Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks,
MADiMa15(458-465).
Springer DOI 1511
BibRef

Li, Y.[Yao], Liu, L.Q.[Ling-Qiao], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Mid-level deep pattern mining,
CVPR15(971-980)
IEEE DOI 1510
Convolutional Neural Networks. BibRef

Wang, M.[Min], Liu, B.Y.[Bao-Yuan], Foroosh, H.[Hassan],
Look-Up Table Unit Activation Function for Deep Convolutional Neural Networks,
WACV18(1225-1233)
IEEE DOI 1806
BibRef
Earlier:
Factorized Convolutional Neural Networks,
Matrix-Tensor17(545-553)
IEEE DOI 1802
Gaussian processes, convolution, data structures, feedforward neural nets, image recognition, interpolation, Training. Complexity theory, Convolution, Kernel, Standards, BibRef

Amthor, M.[Manuel], Rodner, E.[Erik], Denzler, J.[Joachim],
Impatient DNNs: Deep Neural Networks with Dynamic Time Budgets,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Simon, M.[Marcel], Rodner, E.[Erik],
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks,
ICCV15(1143-1151)
IEEE DOI 1602
Birds BibRef

Rodner, E.[Erik], Simon, M.[Marcel], Fisher, R.[Robert], Denzler, J.[Joachim],
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Denzler, J.[Joachim], Rodner, E.[Erik], Simon, M.[Marcel],
Convolutional Neural Networks as a Computational Model for the Underlying Processes of Aesthetics Perception,
CVAA16(I: 871-887).
Springer DOI 1611
BibRef

Simon, M.[Marcel], Rodner, E.[Erik], Denzler, J.[Joachim],
Part Detector Discovery in Deep Convolutional Neural Networks,
ACCV14(II: 162-177).
Springer DOI 1504
BibRef

Ng, J.Y.H.[Joe Yue-Hei], Hausknecht, M.[Matthew], Vijayanarasimhan, S.[Sudheendra], Vinyals, O.[Oriol], Monga, R.[Rajat], Toderici, G.[George],
Beyond short snippets: Deep networks for video classification,
CVPR15(4694-4702)
IEEE DOI 1510
BibRef

Chen, G.B.[Guo-Bin], Han, T.X.[Tony X.], He, Z.H.[Zhi-Hai], Kays, R.[Roland], Forrester, T.[Tavis],
Deep convolutional neural network based species recognition for wild animal monitoring,
ICIP14(858-862)
IEEE DOI 1502
Birds BibRef

Hafemann, L.G.[Luiz G.], Oliveira, L.S.[Luiz S.], Cavalin, P.[Paulo],
Forest Species Recognition Using Deep Convolutional Neural Networks,
ICPR14(1103-1107)
IEEE DOI 1412
Accuracy BibRef

Gatta, C.[Carlo], Romero, A.[Adriana], van de Veijer, J.[Joost],
Unrolling Loopy Top-Down Semantic Feedback in Convolutional Deep Networks,
DeepLearn14(504-511)
IEEE DOI 1409
BibRef

Yin, X.C.[Xu-Cheng], Yang, C.[Chun], Pei, W.Y.[Wei-Yi], Hao, H.W.[Hong-Wei],
Shallow Classification or Deep Learning: An Experimental Study,
ICPR14(1904-1909)
IEEE DOI 1412
Character recognition BibRef

Kekec, T.[Taygun], Emonet, R.[Remi], Fromont, E.[Elisa], Tremeau, A.[Alain], Wolf, C.[Christian],
Contextually Constrained Deep Networks for Scene Labeling,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Zhong, S.H.[Sheng-Hua], Liu, Y.[Yan], Chung, F.L.[Fu-Lai], Wu, G.S.[Gang-Shan],
Semiconducting bilinear deep learning for incomplete image recognition,
ICMR12(32).
DOI Link 1301
semiconducting bilinear deep belief networks (SBDBN) human's visual cortex. BibRef

Ciregan, D.[Dan], Meier, U.[Ueli], Schmidhuber, J.[Jurgen],
Multi-column deep neural networks for image classification,
CVPR12(3642-3649).
IEEE DOI 1208
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Structural Description, Spatial Descriptions in Deep Networks .