14.5.8.6.10 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.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

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

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

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Visualization, Focusing, Visual systems, Biology, Pattern recognition, Convolutional neural networks, History, selective attention BibRef

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Deep neural networks, Embedding, Visual explanations BibRef

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Image retrieval, Noise, Multiple instance learning, Loss functions BibRef

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Amato, G.[Giuseppe], Carrara, F.[Fabio], Falchi, F.[Fabrizio], Gennaro, C.[Claudio], Lagani, G.[Gabriele],
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IEEE DOI 2103
Training, Robustness, Jacobian matrices, Task analysis, Neural networks, Optimization, Deep learning, Deep neural networks, image classification BibRef

Samek, W., Montavon, G., Lapuschkin, S., Anders, C.J., Müller, K.R.,
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Survey, Deep Learning. Deep learning, Systematics, Neural networks, Artificial intelligence, Machine learning, Unsupervised learning, neural networks BibRef

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CVPR20(8937-8946)
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Robustness, Training, Computational modeling, Solid modeling, Computer architecture, Artificial neural networks BibRef

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Deep Nets: What have They Ever Done for Vision?,
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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

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Rotation-Invariant Point Convolution With Multiple Equivariant Alignments.,
3DV20(504-513)
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Convolution, Kernel, Standards, Deep learning, Shape, Neural networks, Deep Learning, Point Clouds, Convolution BibRef

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Zhu, Z., Wang, H.,
Deep Adversarial Active Learning With Model Uncertainty For Image Classification,
ICIP20(1711-1715)
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Task analysis, Uncertainty, Training, Predictive models, Data models, Labeling, Loss measurement, Active learning, Adversarial learning, Image classification BibRef

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)
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Feature extraction, Task analysis, Computer architecture, Object detection, Charge coupled devices, Training, multi-scale BibRef

Oyedotun, O.K., Shabayek, A.E.R., Aouada, D., Ottersten, B.,
Going Deeper With Neural Networks Without Skip Connections,
ICIP20(1756-1760)
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Training, Error analysis, Neural networks, Optimization, Explosions, Computational modeling, Deep neural network, PlainNet, classification BibRef

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

Zhang, X.[Xiao], Zhao, R.[Rui], Qiao, Y.[Yu], Li, H.S.[Hong-Sheng],
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Zhao, J.J.[Jun-Jie], Lu, D.H.[Dong-Huan], Ma, K.[Kai], Zhang, Y.[Yu], Zheng, Y.F.[Ye-Feng],
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Deep Reinforced Attention Learning for Quality-Aware Visual Recognition,
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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)
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Uncertainty, Task analysis, Estimation, Predictive models, Computer vision, 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, Computer vision 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)
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To learn shapes. Shape, Robustness, Decoding, Solid modeling, Training, Neural networks BibRef

Le, H.[Hoang], Liu, F.[Feng], Zhang, S.[Shu], Agarwala, A.[Aseem],
Deep Homography Estimation for Dynamic Scenes,
CVPR20(7649-7658)
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Neural networks, Estimation, Dynamics, Robustness, Training, Cameras, Task analysis 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

Wang, Q., Zhang, L., Wu, B., Ren, D., Li, P., Zuo, W., Hu, Q.,
What Deep CNNs Benefit From Global Covariance Pooling: An Optimization Perspective,
CVPR20(10768-10777)
IEEE DOI 2008
Optimization, Training, Task analysis, Convergence, Robustness, Loss measurement, Stability analysis BibRef

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

Gudovskiy, D., Hodgkinson, A., Yamaguchi, T., Tsukizawa, S.,
Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision,
CVPR20(9038-9046)
IEEE DOI 2008
Task analysis, Training, Kernel, Labeling, Artificial intelligence, Data models, 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, Computer vision, 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, Computer vision, 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, Computer vision 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

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

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

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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],
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Refined Deep Learning for Digital Objects Recognition via Betti Invariants,
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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
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

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

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