Bengio, Y.,
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IEEE DOI
1307
Image edge detection; Convolutional networks;
deep learning; scene parsing
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Learning multiscale and deep representations for classifying remotely
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Elsevier DOI
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Multiscale convolutional neural network (MCNN)
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String representations and distances in deep Convolutional Neural
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PR(54), No. 1, 2016, pp. 104-115.
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1603
Convolutional Neural Network
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Greenspan, H.,
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Guest Editorial Deep Learning in Medical Imaging:
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IEEE DOI
1605
Artificial neural networks
BibRef
Ghesu, F.C.[Florin C.],
Georgescu, B.,
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Grbic, S.,
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Comaniciu, D.,
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark
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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.,
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Deep Decision Network for Multi-class Image Classification,
CVPR16(2240-2248)
IEEE DOI
1612
BibRef
Mansoor, A.,
Cerrolaza, J.J.,
Idrees, R.,
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Alsharid, M.A.,
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Linguraru, M.G.,
Deep Learning Guided Partitioned Shape Model for Anterior Visual
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MedImg(35), No. 8, August 2016, pp. 1856-1865.
IEEE DOI
1608
Biomedical optical imaging
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van Noord, N.[Nanne],
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PR(61), No. 1, 2017, pp. 583-592.
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1609
Convolutional Neural Networks
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Hierarchical deep neural network for multivariate regression,
PR(63), No. 1, 2017, pp. 149-157.
Elsevier DOI
1612
Divide and Conquer
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Nanni, L.[Loris],
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How could a subcellular image, or a painting by Van Gogh, be similar
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Elsevier DOI
1612
Deep convolutional neural networks
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Guo, S.,
Huang, W.,
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IP(26), No. 2, February 2017, pp. 808-820.
IEEE DOI
1702
data compression
BibRef
Masoumi, M.[Majid],
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Spectral shape classification: A deep learning approach,
JVCIR(43), No. 1, 2017, pp. 198-211.
Elsevier DOI
1702
Deep learning
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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
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Luciano, L.[Lorenzo],
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Li, Y.[Yao],
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Springer DOI
1702
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Sun, W.C.[Wei-Chen],
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A novel companion objective function for regularization of deep
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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],
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Li, X.[Xi],
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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],
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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
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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
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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],
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Superpixel-based 3D deep neural networks for hyperspectral image
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PR(74), No. 1, 2018, pp. 600-616.
Elsevier DOI
1711
Hyperspectral image classification
BibRef
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Multiscale Superpixel-Based Hyperspectral Image Classification Using
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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
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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
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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
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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.],
Fernandez-Beltran, R.,
Plaza, J.[Javier],
Plaza, A.J.[Antonio J.],
Li, J.,
Pla, F.,
Capsule Networks for Hyperspectral Image Classification,
GeoRS(57), No. 4, April 2019, pp. 2145-2160.
IEEE DOI
1904
convolutional neural nets, hyperspectral imaging,
image classification, learning (artificial intelligence),
hyperspectral imaging (HSI)
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.,
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)
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
Xiang, C.,
Zhang, L.,
Tang, Y.,
Zou, W.,
Xu, C.,
MS-CapsNet: A Novel Multi-Scale Capsule Network,
SPLetters(25), No. 12, December 2018, pp. 1850-1854.
IEEE DOI
1812
feature extraction, image classification, image representation,
network theory (graphs), image classification,
deep learning
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
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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
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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
Vijay Kumar, B.G.,
Carneiro, G.[Gustavo],
Reid, I.D.[Ian D.],
Learning Local Image Descriptors with Deep Siamese and Triplet
Convolutional Networks by Minimizing Global Loss Functions,
CVPR16(5385-5394)
IEEE DOI
1612
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
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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
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
Forcén, J.I.[Juan Ignacio],
Pagola, M.[Miguel],
Barrenechea, E.[Edurne],
Bustince, H.[Humberto],
Aggregation of Deep Features for Image Retrieval Based on Object
Detection,
IbPRIA19(I:553-564).
Springer DOI
1910
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
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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
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
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.,
Rodolà, E.,
Svoboda, J.,
Bronstein, M.M.[Michael M.],
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model
CNNs,
CVPR17(5425-5434)
IEEE DOI
1711
Computational modeling, Convolution, Laplace equations,
Machine learning, Manifolds, Shape,
BibRef
Gordo, A.[Albert],
Gaidon, A.[Adrien],
Perronnin, F.[Florent],
Deep Fishing: Gradient Features from Deep Nets,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Thewlis, J.[James],
Zheng, S.[Shuai],
Torr, P.H.S.[Philip H.S.],
Vedaldi, A.[Andrea],
Fully-trainable deep matching,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Bilen, H.,
Vedaldi, A.,
Weakly Supervised Deep Detection Networks,
CVPR16(2846-2854)
IEEE DOI
1612
BibRef
Yang, M.[Mu],
Li, B.[Brian],
Fan, H.Q.[Hao-Qiang],
Jiang, Y.N.[Yu-Ning],
Randomized spatial pooling in deep convolutional networks for scene
recognition,
ICIP15(402-406)
IEEE DOI
1512
deep convolutional networks
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
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Talathi, S.S.[Sachin S.],
Hyper-parameter optimization of deep convolutional networks for
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ICIP15(3982-3986)
IEEE DOI
1512
deep convolution networks
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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
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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
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Li, Y.[Yao],
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Mid-level deep pattern mining,
CVPR15(971-980)
IEEE DOI
1510
Convolutional Neural Networks.
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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
Yoo, D.G.[Dong-Geun],
Park, S.G.[Sung-Gyun],
Lee, J.Y.[Joon-Young],
Kweon, I.S.[In So],
Multi-scale pyramid pooling for deep convolutional representation,
DeepLearn15(71-80)
IEEE DOI
1510
Accuracy
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Denzler, J.[Joachim],
Impatient DNNs: Deep Neural Networks with Dynamic Time Budgets,
BMVC16(xx-yy).
HTML Version.
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Simon, M.[Marcel],
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Neural Activation Constellations: Unsupervised Part Model Discovery
with Convolutional Networks,
ICCV15(1143-1151)
IEEE DOI
1602
Birds
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Rodner, E.[Erik],
Simon, M.[Marcel],
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Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of
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Convolutional Neural Networks as a Computational Model for the
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CVAA16(I: 871-887).
Springer DOI
1611
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Simon, M.[Marcel],
Rodner, E.[Erik],
Denzler, J.[Joachim],
Part Detector Discovery in Deep Convolutional Neural Networks,
ACCV14(II: 162-177).
Springer DOI
1504
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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
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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
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ICIP14(858-862)
IEEE DOI
1502
Birds
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Hafemann, L.G.[Luiz G.],
Oliveira, L.S.[Luiz S.],
Cavalin, P.[Paulo],
Forest Species Recognition Using Deep Convolutional Neural Networks,
ICPR14(1103-1107)
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1412
Accuracy
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Unrolling Loopy Top-Down Semantic Feedback in Convolutional Deep
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DeepLearn14(504-511)
IEEE DOI
1409
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Gong, Y.C.[Yun-Chao],
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Multi-scale Orderless Pooling of Deep Convolutional Activation Features,
ECCV14(VII: 392-407).
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1408
Deep convolutional neural networks
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Shallow Classification or Deep Learning: An Experimental Study,
ICPR14(1904-1909)
IEEE DOI
1412
Character recognition
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Kekec, T.[Taygun],
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Semiconducting bilinear deep learning for incomplete image recognition,
ICMR12(32).
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semiconducting bilinear deep belief networks (SBDBN)
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Multi-column deep neural networks for image classification,
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IEEE DOI
1208
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Networks Applied to Specific Problems .