14.5.8.5.8 Deep Learning, Deep Nets

Chapter Contents (Back)
Deep Nets. Neural Networks.

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

Farabet, C.[Clement], Couprie, C.[Camille], Najman, L.[Laurent], Le Cun, Y.L.[Yann L.],
Learning Hierarchical Features for Scene Labeling,
PAMI(35), No. 8, 2013, pp. 1915-1929.
IEEE DOI 1307
Image edge detection; Convolutional networks; deep learning; scene parsing BibRef

Zhao, W.[Wenzhi], 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

Nanni, L.[Loris], Ghidoni, S.[Stefano],
How could a subcellular image, or a painting by Van Gogh, be similar to a great white shark or to a pizza?,
PRL(85), No. 1, 2017, pp. 1-7.
Elsevier DOI 1612
Deep convolutional neural networks 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

Ioannidou, A.[Anastasia], Chatzilari, E.[Elisavet], Nikolopoulos, S.[Spiros], Kompatsiaris, I.[Ioannis],
Deep Learning Advances in Computer Vision with 3D Data: A Survey,
Surveys(50), No. 2, June 2017, pp. Article No 20.
DOI Link 1708
Survey, Deep Learning. This article surveys methods applying deep learning on 3D data and provides a classification based on how they exploit them. From the results of the examined works, we conclude that systems employing 2D views of 3D data typically surpass voxel-based (3D) deep models, which however, can perform better with more layers and severe data augmentation. Therefore, larger-scale datasets and increased resolutions are required. BibRef

McCann, M.T., Jin, K.H., Unser, M.,
Convolutional Neural Networks for Inverse Problems in Imaging: A Review,
SPMag(34), No. 6, November 2017, pp. 85-95.
IEEE DOI 1712
Survey, Convolutional Neural Networks. Computed tomography, Image reconstruction, Image resolution, Image segmentation, Inverse problems, Linear programming, Noise reduction BibRef

Jin, K.H., McCann, M.T., Froustey, E., Unser, M.,
Deep Convolutional Neural Network for Inverse Problems in Imaging,
IP(26), No. 9, September 2017, pp. 4509-4522.
IEEE DOI 1708
computerised tomography, feedforward neural nets, image resolution, iterative methods, learning (artificial intelligence), medical image processing, 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

Arulkumaran, K., Deisenroth, M.P., Brundage, M., Bharath, A.A.,
Deep Reinforcement Learning: A Brief Survey,
SPMag(34), No. 6, November 2017, pp. 26-38.
IEEE DOI 1712
Survey, Deep Learning. Artificial intelligence, Learning (artificial intelligence), Machine learning, Neural networks, Signal processing algorithms, Visualization BibRef

Fawzi, A.[Alhussein], Moosavi-Dezfooli, S.M.[Seyed-Mohsen], Frossard, P.[Pascal],
The Robustness of Deep Networks: A Geometrical Perspective,
SPMag(34), No. 6, November 2017, pp. 50-62.
IEEE DOI 1712
Classification, Machine learning, Neural networks, Robustness, Visualization 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

Haut, J.M.[Juan M.], Paoletti, M.E.[Mercedes E.], Plaza, J.[Javier], Li, J.[Jun], Plaza, A.J.[Antonio J.],
Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach,
GeoRS(56), No. 11, November 2018, pp. 6440-6461.
IEEE DOI 1811
Hyperspectral imaging, Feature extraction, Training, Bayes methods, Imaging, Neural networks, Active learning (AL), hyperspectral remote sensing image classification BibRef

Paoletti, M.E.[Mercedes E.], Haut, J.M.[Juan M.], Plaza, J.[Javier], Plaza, A.J.[Antonio J.],
Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Paoletti, M.E.[Mercedes E.], Haut, J.M.[Juan M.], Plaza, J.[Javier], Plaza, A.J.[Antonio J.],
A new deep convolutional neural network for fast hyperspectral image classification,
PandRS(145), 2018, pp. 120-147.
Elsevier DOI 1810
Hyperspectral imaging, Deep learning, Convolutional neural networks (CNNs), Classification, Graphics processing units (GPUs) BibRef

Paoletti, M.E.[Mercedes E.], Haut, J.M.[Juan M.], Fernandez-Beltran, R., Plaza, J.[Javier], Plaza, A.J.[Antonio J.], Pla, F.[Filiberto],
Deep Pyramidal Residual Networks for Spectral-Spatial Hyperspectral Image Classification,
GeoRS(57), No. 2, February 2019, pp. 740-754.
IEEE DOI 1901
Feature extraction, Hyperspectral imaging, Machine learning, Data models, Training, Convolutional neural networks (CNNs), residual networks (ResNets) See also Capsule Networks for Hyperspectral Image Classification. 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

Yang, J.[Jihai], Xiong, W.[Wei], Li, S.J.[Shi-Jun], Xu, C.[Chang],
Learning structured and non-redundant representations with deep neural networks,
PR(86), 2019, pp. 224-235.
Elsevier DOI 1811
Deep networks, Overfitting, Decorrelation 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

Bazi, Y.[Yakoub], Rahhal, M.M.A.[Mohamad M. Al], Alhichri, H.[Haikel], Alajlan, N.[Naif],
Simple Yet Effective Fine-Tuning of Deep CNNs Using an Auxiliary Classification Loss for Remote Sensing Scene Classification,
RS(11), No. 24, 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, Computer vision 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

Das, R.[Rangan], Sen, S.[Sagnik], Maulik, U.[Ujjwal],
A Survey on Fuzzy Deep Neural Networks,
Surveys(53), No. 3, May 2020, pp. xx-yy.
DOI Link 2007
Survey, Deep Networks. parallel models, integrated models, sequential models, ensemble models, fuzzy systems, Deep architecture 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


Phan, H.[Hai], Huynh, D.[Dang], He, Y.[Yihui], Savvides, M.[Marios], Shen, Z.Q.[Zhi-Qiang],
MoBiNet: A Mobile Binary Network for Image Classification,
WACV20(3442-3451)
IEEE DOI 2006
Neural networks, Training, Computer architecture, Computational modeling, Standards, Task analysis, Computational efficiency 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

Xu, Y., Xu, D., Hong, X., Ouyang, W., Ji, R., Xu, M., Zhao, G.,
Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection,
ICCV19(3788-3797)
IEEE DOI 2004
convolutional neural nets, learning (artificial intelligence), message passing, object detection, structured modeling 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

Zhu, X., Cheng, D., Zhang, Z., Lin, S., Dai, J.,
An Empirical Study of Spatial Attention Mechanisms in Deep Networks,
ICCV19(6687-6696)
IEEE DOI 2004
convolution, image retrieval, neural nets, spatial attention mechanisms, deep neural networks, Natural language processing 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, Computer vision 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

Rashwan, A., Kalra, A., Poupart, P.,
Matrix Nets: A New Deep Architecture for Object Detection,
NeruArch19(2025-2028)
IEEE DOI 2004
learning (artificial intelligence), neural net architecture, object detection, Matrix Nets, deep architecture, object detection, neural architecture 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
BibRef

Hossain, M.T.[Md Tahmid], Teng, S.W.[Shyh Wei], Zhang, D.S.[Deng-Sheng], Lim, S.[Suryani], Lu, G.[Guojun],
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
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

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
BibRef

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
BibRef

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, Computer vision, 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
Computer vision, 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

Huang, Z.[Zehao], Wang, N.[Naiyan],
Data-Driven Sparse Structure Selection for Deep Neural Networks,
ECCV18(XVI: 317-334).
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
BibRef

Qi, C., Su, F.,
Contrastive-center loss for deep neural networks,
ICIP17(2851-2855)
IEEE DOI 1803
Face recognition, Feature extraction, Neural networks, Task analysis, Testing, Training, Visualization, Auxiliary loss, Image classification and face recognition BibRef

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

Mughees, A., Tao, L.,
Spectral-Spatial Hyperspectral Image Classification via Boundary-Adaptive Deep Learning,
DICTA17(1-6)
IEEE DOI 1804
BibRef
And:
Hyper-voxel based deep learning for hyperspectral image classification,
ICIP17(840-844)
IEEE DOI 1803
geophysical image processing, Training. feature extraction, hyperspectral imaging, image classification, image segmentation, learning (artificial intelligence), stacked auto-encoder BibRef

Mughees, A., Ali, A., Tao, L.,
Hyperspectral image classification via shape-adaptive deep learning,
ICIP17(375-379)
IEEE DOI 1803
Feature extraction, Hyperspectral imaging, Image segmentation, Machine learning, Spatial resolution, Training, segmentation 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

Chang, J.L.[Jian-Long], Wang, L.F.[Ling-Feng], Meng, G.F.[Gao-Feng], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Deep Adaptive Image Clustering,
ICCV17(5880-5888)
IEEE DOI 1802
convolution, feature extraction, image classification, iterative methods, Training BibRef

Li, Y.S.[Yun-Sheng], Dixit, M.[Mandar], Vasconcelos, N.M.[Nuno M.],
Deep Scene Image Classification with the MFAFVNet,
ICCV17(5757-5765)
IEEE DOI 1802
Computational modeling, Covariance matrices, Feature extraction, Neural networks, Training 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

Schultheiss, A.[Alexander], Käding, C.[Christoph], Freytag, A.[Alexander], Denzler, J.[Joachim],
Finding the Unknown: Novelty Detection with Extreme Value Signatures of Deep Neural Activations,
GCPR17(226-238).
Springer DOI 1711
Which level of CNN has extreme values. 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

Gidaris, S.[Spyros], Komodakis, N.[Nikos],
Detect, Replace, Refine: Deep Structured Prediction for Pixel Wise Labeling,
CVPR17(7187-7196)
IEEE DOI 1711
Estimation, Iron, Labeling, Neural networks, Predictive, models BibRef

Yang, S.J.[Shi-Jie], Li, L.[Liang], Wang, S.H.[Shu-Hui], Zhang, W.G.[Wei-Gang], Huang, Q.M.[Qing-Ming],
A Graph Regularized Deep Neural Network for Unsupervised Image Representation Learning,
CVPR17(7053-7061)
IEEE DOI 1711
Decoding, Image reconstruction, Laplace equations, Manifolds, Neural networks, Robustness, Semantics BibRef

Sun, F.C.[Fu-Chun], Kong, T.[Tao], Huang, W.B.[Wen-Bing], Tan, C.Q.[Chuan-Qi], Fang, B.[Bin], Liu, H.P.[Hua-Ping],
Feature Pyramid Reconfiguration With Consistent Loss for Object Detection,
IP(28), No. 10, October 2019, pp. 5041-5051.
IEEE DOI 1909
BibRef
Earlier: A2, A1, A3, A6, Only:
Deep Feature Pyramid Reconfiguration for Object Detection,
ECCV18(VI: 172-188).
Springer DOI 1810
Object detection, Detectors, Feature extraction, Semantics, Training, Proposals, Entropy, Accurate object detection, consistent loss 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

Jia, K., Tao, D., Gao, S., Xu, X.,
Improving Training of Deep Neural Networks via Singular Value Bounding,
CVPR17(3994-4002)
IEEE DOI 1711
Histograms, Machine learning, Neural networks, Optimization, Standards, Training 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

Hou, X.X.[Xian-Xu], Shen, L.L.[Lin-Lin], Sun, K.[Ke], Qiu, G.P.[Guo-Ping],
Deep Feature Consistent Variational Autoencoder,
WACV17(1133-1141)
IEEE DOI 1609
Correlation, Decoding, Face, Feature extraction, Image reconstruction, Loss measurement, Training BibRef

Wang, B., Yager, K., Yu, D., Hoai, M.,
X-Ray Scattering Image Classification Using Deep Learning,
WACV17(697-704)
IEEE DOI 1609
Feature extraction, Machine learning, Neural networks, Scattering, Training, X-ray imaging, X-ray, scattering BibRef

Zhao, J.P.[Jia-Ping], Itti, L.[Laurent],
Improved Deep Learning of Object Category Using Pose Information,
WACV17(550-559)
IEEE DOI 1609
Biological neural networks, Cameras, Convolution, Lighting, Neurons, Object recognition, Training 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
BibRef

Sajjadi, M., Javanmardi, M., Tasdizen, T.,
Mutual exclusivity loss for semi-supervised deep learning,
ICIP16(1908-1912)
IEEE DOI 1610
Entropy BibRef

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
BibRef
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., Rodolŕ, 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
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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

Yoo, D.G.[Dong-Geun], Kweon, I.S.[In So],
Learning Loss for Active Learning,
CVPR19(93-102).
IEEE DOI 2002
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.[Zhihai], 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
Siamese Networks .


Last update:Aug 4, 2020 at 13:31:31