Bengio, Y.,
Learning deep architectures for AI,
FTML(1), No. 1, 2009, pp. 1-127.
DOI Link
1609
BibRef
Zhao, W.Z.[Wen-Zhi],
Du, S.H.[Shi-Hong],
Learning multiscale and deep representations for classifying remotely
sensed imagery,
PandRS(113), No. 1, 2016, pp. 155-165.
Elsevier DOI
1602
Multiscale convolutional neural network (MCNN)
BibRef
Barat, C.[Cécile],
Ducottet, C.[Christophe],
String representations and distances in deep Convolutional Neural
Networks for image classification,
PR(54), No. 1, 2016, pp. 104-115.
Elsevier DOI
1603
Convolutional Neural Network
BibRef
Greenspan, H.,
van Ginneken, B.,
Summers, R.M.,
Guest Editorial Deep Learning in Medical Imaging:
Overview and Future Promise of an Exciting New Technique,
MedImg(35), No. 5, May 2016, pp. 1153-1159.
IEEE DOI
1605
Artificial neural networks
BibRef
Ghesu, F.C.[Florin C.],
Georgescu, B.,
Zheng, Y.,
Grbic, S.,
Maier, A.,
Hornegger, J.[Joachim],
Comaniciu, D.,
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark
Detection in CT Scans,
PAMI(41), No. 1, January 2019, pp. 176-189.
IEEE DOI
1812
Machine learning, Biomedical imaging, Search problems, Training,
Real-time systems, Deep learning,
intelligent localization
BibRef
Murthy, V.N.,
Singh, V.,
Chen, T.,
Manmatha, R.,
Comaniciu, D.,
Deep Decision Network for Multi-class Image Classification,
CVPR16(2240-2248)
IEEE DOI
1612
BibRef
Mansoor, A.,
Cerrolaza, J.J.,
Idrees, R.,
Biggs, E.,
Alsharid, M.A.,
Avery, R.A.,
Linguraru, M.G.,
Deep Learning Guided Partitioned Shape Model for Anterior Visual
Pathway Segmentation,
MedImg(35), No. 8, August 2016, pp. 1856-1865.
IEEE DOI
1608
Biomedical optical imaging
BibRef
van Noord, N.[Nanne],
Postma, E.[Eric],
Learning scale-variant and scale-invariant features for deep image
classification,
PR(61), No. 1, 2017, pp. 583-592.
Elsevier DOI
1609
Convolutional Neural Networks
BibRef
Du, J.[Jun],
Xu, Y.[Yong],
Hierarchical deep neural network for multivariate regression,
PR(63), No. 1, 2017, pp. 149-157.
Elsevier DOI
1612
Divide and Conquer
BibRef
Guo, S.,
Huang, W.,
Wang, L.,
Qiao, Y.,
Locally Supervised Deep Hybrid Model for Scene Recognition,
IP(26), No. 2, February 2017, pp. 808-820.
IEEE DOI
1702
data compression
BibRef
Masoumi, M.[Majid],
Ben Hamza, A.,
Spectral shape classification: A deep learning approach,
JVCIR(43), No. 1, 2017, pp. 198-211.
Elsevier DOI
1702
Deep learning
BibRef
Luciano, L.[Lorenzo],
Ben Hamza, A.,
Deep learning with geodesic moments for 3D shape classification,
PRL(105), 2018, pp. 182-190.
Elsevier DOI
1804
Geodesic moments, Deep learning, Laplace-Beltrami,
Stacked autoencoders, Shape classification
BibRef
Luciano, L.[Lorenzo],
Ben Hamza, A.,
Deep similarity network fusion for 3D shape classification,
VC(35), No. 6-8, June 2018, pp. 1171-1180.
WWW Link.
1906
BibRef
Li, Y.[Yao],
Liu, L.Q.[Ling-Qiao],
Shen, C.H.[Chun-Hua],
van den Hengel, A.J.[Anton J.],
Mining Mid-level Visual Patterns with Deep CNN Activations,
IJCV(121), No. 3, February 2017, pp. 344-364.
Springer DOI
1702
BibRef
Sun, W.C.[Wei-Chen],
Su, F.[Fei],
A novel companion objective function for regularization of deep
convolutional neural networks,
IVC(60), No. 1, 2017, pp. 58-63.
Elsevier DOI
1704
BibRef
Earlier:
Regularization of deep neural networks using a novel companion
objective function,
ICIP15(2865-2869)
IEEE DOI
1512
Convolutional neural network.
Companion objective function
BibRef
Sun, W.C.[Wei-Chen],
Su, F.[Fei],
Wang, L.Q.[Lei-Quan],
Improving deep neural networks with multilayer maxout networks,
VCIP14(334-337)
IEEE DOI
1504
filtering theory
BibRef
Wu, F.[Fei],
Wang, Z.H.[Zhu-Hao],
Lu, W.M.[Wei-Ming],
Li, X.[Xi],
Yang, Y.[Yi],
Luo, J.B.[Jie-Bo],
Zhuang, Y.T.[Yue-Ting],
Regularized Deep Belief Network for Image Attribute Detection,
CirSysVideo(27), No. 7, July 2017, pp. 1464-1477.
IEEE DOI
1707
Computational modeling, Context modeling, Correlation,
Feature extraction, Neural networks, Semantics, Training,
Contextual correlation, deep belief network (DBN), deep learning,
image, attribute
BibRef
Lee, H.,
Kwon, H.,
Going Deeper With Contextual CNN for Hyperspectral Image
Classification,
IP(26), No. 10, October 2017, pp. 4843-4855.
IEEE DOI
1708
geophysical image processing, hyperspectral imaging,
image classification, neural nets,
CNN-based hyperspectral image classification,
contextual deep CNN, joint spatio-spectral feature map,
local contextual interactions,
multiscale convolutional filter bank,
Biological neural networks, Feature extraction,
Hyperspectral imaging, Principal component analysis, Training,
hyperspectral image classification, multi-scale filter bank,
BibRef
Shi, C.[Cheng],
Pun, C.M.[Chi-Man],
Superpixel-based 3D deep neural networks for hyperspectral image
classification,
PR(74), No. 1, 2018, pp. 600-616.
Elsevier DOI
1711
Hyperspectral image classification
BibRef
Shi, C.[Cheng],
Pun, C.M.[Chi-Man],
Multiscale Superpixel-Based Hyperspectral Image Classification Using
Recurrent Neural Networks With Stacked Autoencoders,
MultMed(22), No. 2, February 2020, pp. 487-501.
IEEE DOI
2001
Recurrent neural networks, Correlation, Feature extraction,
Neurons, Hyperspectral imaging, Principal component analysis,
Stacked autoencoders
BibRef
Wang, K.[Keze],
Zhang, D.Y.[Dong-Yu],
Li, Y.[Ya],
Zhang, R.M.[Rui-Mao],
Lin, L.[Liang],
Cost-Effective Active Learning for Deep Image Classification,
CirSysVideo(27), No. 12, December 2017, pp. 2591-2600.
IEEE DOI
1712
Labeling, Learning systems, Measurement uncertainty,
Neural networks, Training, Uncertainty, Visualization,
incremental learning
BibRef
Zagoruyko, S.[Sergey],
Komodakis, N.[Nikos],
Deep compare: A study on using convolutional neural networks to
compare image patches,
CVIU(164), No. 1, 2017, pp. 38-55.
Elsevier DOI
1801
BibRef
Earlier:
Learning to compare image patches via convolutional neural networks,
CVPR15(4353-4361)
IEEE DOI
1510
Descriptor learning
BibRef
Simonovsky, M.[Martin],
Komodakis, N.[Nikos],
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on
Graphs,
CVPR17(29-38)
IEEE DOI
1711
Convolution, Machine learning, Neural networks, Spectral analysis,
Standards,
BibRef
Simonovsky, M.[Martin],
Komodakis, N.[Nikos],
OnionNet: Sharing Features in Cascaded Deep Classifiers,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Liu, N.[Na],
Lu, X.[Xiankai],
Wan, L.[Lihong],
Huo, H.[Hong],
Fang, T.[Tao],
Improving the Separability of Deep Features with Discriminative
Convolution Filters for RSI Classification,
IJGI(7), No. 3, 2018, pp. xx-yy.
DOI Link
1804
BibRef
Liu, Y.F.[Yan-Fei],
Zhong, Y.F.[Yan-Fei],
Fei, F.[Feng],
Zhu, Q.[Qiqi],
Qin, Q.Q.[Qian-Qing],
Scene Classification Based on a Deep Random-Scale Stretched
Convolutional Neural Network,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link
1804
BibRef
Liu, Y.F.[Yan-Fei],
Zhong, Y.F.[Yan-Fei],
Qin, Q.Q.[Qian-Qing],
Scene Classification Based on Multiscale Convolutional Neural Network,
GeoRS(56), No. 12, December 2018, pp. 7109-7121.
IEEE DOI
1812
Feature extraction, Remote sensing, Semantics,
Convolutional neural networks, Training, Machine learning,
similarity measure
BibRef
Zhou, Y.C.[Yu-Can],
Hu, Q.H.[Qing-Hua],
Wang, Y.[Yu],
Deep super-class learning for long-tail distributed image
classification,
PR(80), 2018, pp. 118-128.
Elsevier DOI
1805
Super-class construction, Block-structured sparsity,
Deep learning, Long-tail distribution
BibRef
Liu, Y.[Yu],
Liu, L.[Li],
Guo, Y.M.[Yan-Ming],
Lew, M.S.[Michael S.],
Learning visual and textual representations for multimodal matching
and classification,
PR(84), 2018, pp. 51-67.
Elsevier DOI
1809
Vision and language, Multimodal matching,
Multimodal classification, Deep learning
BibRef
Ben Hamida, A.,
Benoit, A.,
Lambert, P.,
Ben Amar, C.,
3-D Deep Learning Approach for Remote Sensing Image Classification,
GeoRS(56), No. 8, August 2018, pp. 4420-4434.
IEEE DOI
1808
geophysical image processing, hyperspectral imaging,
image classification, learning (artificial intelligence),
remote sensing (RS)
BibRef
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.],
Adaptable Convolutional Network for Hyperspectral Image
Classification,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link
2109
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
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
Shin, D.,
Yoo, H.,
The Heterogeneous Deep Neural Network Processor With a Non-von
Neumann Architecture,
PIEEE(108), No. 8, August 2020, pp. 1245-1260.
IEEE DOI
2007
Computer architecture, Hardware, Biological neural networks,
Computers, Recurrent neural networks, Deep learning,
recurrent neural networks (RNNs)
BibRef
Zhu, R.,
Dornaika, F.,
Ruichek, Y.,
Semi-supervised elastic manifold embedding with deep learning
architecture,
PR(107), 2020, pp. 107425.
Elsevier DOI
2008
Graph-based embedding, Elastic embedding,
Deep learning architecture, Supervised learning, Semi-supervised learning
BibRef
Amjad, R.A.[Rana Ali],
Geiger, B.C.[Bernhard C.],
Learning Representations for Neural Network-Based Classification
Using the Information Bottleneck Principle,
PAMI(42), No. 9, September 2020, pp. 2225-2239.
IEEE DOI
2008
Training, Task analysis, Robustness, Cost function, Neurons,
Neural networks, Deep learning, information bottleneck,
stochastic neural networks
BibRef
Chen, Z.[Zhi],
Ho, P.H.[Pin-Han],
A generic shift-norm-activation approach for deep learning,
PR(109), 2021, pp. 107609.
Elsevier DOI
2009
Activation, Normalization, CNN, Shifting, Deep learning
BibRef
Pretorius, A.[Arnu],
Kamper, H.[Herman],
Kroon, S.[Steve],
On the expected behaviour of noise regularised deep neural networks
as Gaussian processes,
PRL(138), 2020, pp. 75-81.
Elsevier DOI
2010
Neural networks, Gaussian processes, Signal propagation, Noise regularisation
BibRef
Santiago, C.[Carlos],
Barata, C.[Catarina],
Sasdelli, M.[Michele],
Carneiro, G.[Gustavo],
Nascimento, J.C.[Jacinto C.],
LOW: Training deep neural networks by learning optimal sample weights,
PR(110), 2021, pp. 107585.
Elsevier DOI
2011
Deep learning, Sample weighting, Imbalanced data sets
BibRef
Dixit, M.[Mandar],
Li, Y.S.[Yun-Sheng],
Vasconcelos, N.M.[Nuno M.],
Semantic Fisher Scores for Task Transfer:
Using Objects to Classify Scenes,
PAMI(42), No. 12, December 2020, pp. 3102-3118.
IEEE DOI
2011
BibRef
And: A2, A1, A3:
Deep Scene Image Classification with the MFAFVNet,
ICCV17(5757-5765)
IEEE DOI
1802
Semantics, Neural networks, Training data, Object recognition,
Neural networks, Computational modeling, Probability, MFA.
Computational modeling, Covariance matrices,
Feature extraction, Training
BibRef
Chen, G.[Gang],
Srihari, S.N.[Sargur N.],
Revisiting hierarchy:
Deep learning with orthogonally constrained prior for classification,
PRL(140), 2020, pp. 214-221.
Elsevier DOI
2012
Hierarchical prior, Classification, Deep learning
BibRef
Luo, P.[Ping],
Zhang, R.M.[Rui-Mao],
Ren, J.M.[Jia-Min],
Peng, Z.L.[Zhang-Lin],
Li, J.Y.[Jing-Yu],
Switchable Normalization for Learning-to-Normalize Deep
Representation,
PAMI(43), No. 2, February 2021, pp. 712-728.
IEEE DOI
2101
Task analysis, Training, Graphics processing units, Switches,
Object detection, Image segmentation, Head, Deep learning,
semantic segmentation and face verification
BibRef
Ma, W.P.[Wen-Ping],
Zhao, J.[Jiliang],
Zhu, H.[Hao],
Shen, J.C.[Jian-Chao],
Jiao, L.C.[Li-Cheng],
Wu, Y.[Yue],
Hou, B.[Biao],
A Spatial-Channel Collaborative Attention Network for Enhancement of
Multiresolution Classification,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Fukushima, K.,
Artificial Vision by Deep CNN Neocognitron,
SMCS(51), No. 1, January 2021, pp. 76-90.
IEEE DOI
2101
Visualization, Focusing, Visual systems, Biology,
Pattern recognition, Convolutional neural networks, History,
selective attention
BibRef
Qi, Z.A.[Zhong-Ang],
Khorram, S.[Saeed],
Fuxin, L.[Li],
Embedding deep networks into visual explanations,
AI(292), 2021, pp. 103435.
Elsevier DOI
2102
Deep neural networks, Embedding, Visual explanations
BibRef
Messina, N.[Nicola],
Amato, G.[Giuseppe],
Carrara, F.[Fabio],
Gennaro, C.[Claudio],
Falchi, F.[Fabrizio],
Solving the same-different task with convolutional neural networks,
PRL(143), 2021, pp. 75-80.
Elsevier DOI
2102
AI, Deep learning, Abstract reasoning, Relational reasoning,
Convolutional neural, Networks
BibRef
Amato, G.[Giuseppe],
Carrara, F.[Fabio],
Falchi, F.[Fabrizio],
Gennaro, C.[Claudio],
Lagani, G.[Gabriele],
Hebbian Learning Meets Deep Convolutional Neural Networks,
CIAP19(I:324-334).
Springer DOI
1909
BibRef
Yan, Z.[Ziang],
Guo, Y.[Yiwen],
Zhang, C.S.[Chang-Shui],
Adversarial Margin Maximization Networks,
PAMI(43), No. 4, April 2021, pp. 1129-1139.
IEEE DOI
2103
Perturbation methods, Training, Support vector machines,
Distortion, Radio frequency, Robustness, Neural networks,
deep neural networks
BibRef
Li, S.[Shuai],
Jia, K.[Kui],
Wen, Y.X.[Yu-Xin],
Liu, T.L.[Tong-Liang],
Tao, D.C.[Da-Cheng],
Orthogonal Deep Neural Networks,
PAMI(43), No. 4, April 2021, pp. 1352-1368.
IEEE DOI
2103
Training, Robustness, Jacobian matrices, Task analysis,
Neural networks, Optimization, Deep learning, Deep neural networks,
image classification
BibRef
Cai, H.Y.[Hua-Yue],
Zhang, X.[Xiang],
Lan, L.[Long],
Dong, G.[Guohua],
Xu, C.F.[Chuan-Fu],
Liu, X.W.[Xin-Wang],
Luo, Z.G.[Zhi-Gang],
Learning deep discriminative embeddings via joint rescaled features
and log-probability centers,
PR(114), 2021, pp. 107852.
Elsevier DOI
2103
Deep discriminative embedding, Softmax loss, Easing overfitting
BibRef
Kortylewski, A.[Adam],
Liu, Q.[Qing],
Wang, A.T.[Ang-Tian],
Sun, Y.H.[Yi-Hong],
Yuille, A.L.[Alan L.],
Compositional Convolutional Neural Networks:
A Robust and Interpretable Model for Object Recognition Under Occlusion,
IJCV(129), No. 3, March 2021, pp. 736-760.
Springer DOI
2103
BibRef
Kortylewski, A.,
He, J.,
Liu, Q.,
Yuille, A.L.,
Compositional Convolutional Neural Networks:
A Deep Architecture With Innate Robustness to Partial Occlusion,
CVPR20(8937-8946)
IEEE DOI
2008
Robustness, Training, Computational modeling, Solid modeling,
Computer architecture, Artificial neural networks
BibRef
Yuille, A.L.[Alan L.],
Liu, C.X.[Chen-Xi],
Deep Nets: What have They Ever Done for Vision?,
IJCV(129), No. 3, March 2021, pp. 781-802.
Springer DOI
2103
We argue that Deep Nets in their current form are unlikely to be able
to overcome the fundamental problem of computer vision, namely how to
deal with the combinatorial explosion, caused by the enormous
complexity of natural images, and obtain the rich understanding of
visual scenes that the human visual achieves.
BibRef
Balestriero, R.[Randall],
Baraniuk, R.G.[Richard G.],
Mad Max: Affine Spline Insights Into Deep Learning,
PIEEE(109), No. 5, May 2021, pp. 704-727.
IEEE DOI
2105
Splines (mathematics), Standards, Deep learning, Convolution,
Task analysis, Recurrent neural networks, Quantization (signal),
Voronoi diagram
BibRef
Liu, D.[Defu],
Ning, J.[Jin],
Wu, J.Z.[Jin-Zhao],
Yang, G.W.[Guo-Wu],
Extending Ordinary-Label Learning Losses to Complementary-Label
Learning,
SPLetters(28), 2021, pp. 852-856.
IEEE DOI
2106
Training, Neural networks, Signal processing algorithms,
Risk management, Mean square error methods, Machine learning,
deep neural networks
BibRef
Zeng, X.F.[Xian-Fang],
Wu, W.X.[Wen-Xuan],
Tian, G.Z.[Guang-Zhong],
Li, F.X.[Fu-Xin],
Liu, Y.[Yong],
Deep Superpixel Convolutional Network for Image Recognition,
SPLetters(28), 2021, pp. 922-926.
IEEE DOI
2106
Convolution, Task analysis, Standards,
Image recognition, Kernel, Feature extraction, Deep learning,
superpixel
BibRef
Zhao, B.X.[Bao-Xin],
Xiong, H.Y.[Hao-Yi],
Bian, J.[Jiang],
Guo, Z.S.[Zhi-Shan],
Xu, C.Z.[Cheng-Zhong],
Dou, D.[Dejing],
COMO: Efficient Deep Neural Networks Expansion With COnvolutional
MaxOut,
MultMed(23), 2021, pp. 1722-1730.
IEEE DOI
2106
Convolution, Spatial resolution, Convolutional neural networks,
Deep learning, Computer architecture, Transforms, neural networks
BibRef
Wang, Q.L.[Qi-Long],
Xie, J.T.[Jiang-Tao],
Zuo, W.M.[Wang-Meng],
Zhang, L.[Lei],
Li, P.H.[Pei-Hua],
Deep CNNs Meet Global Covariance Pooling:
Better Representation and Generalization,
PAMI(43), No. 8, August 2021, pp. 2582-2597.
IEEE DOI
2107
Covariance matrices, Robustness, Estimation, Geometry, Measurement,
Visualization, Complexity theory, Global covariance pooling,
visual recognition
BibRef
Chen, Z.[Zhe],
Wu, X.J.[Xiao-Jun],
Xu, T.Y.[Tian-Yang],
Kittler, J.V.[Josef V.],
Learning Alternating Deep-Layer Cascaded Representation,
SPLetters(28), 2021, pp. 1520-1524.
IEEE DOI
2108
Training, Encoding, Collaboration, Mathematical model,
Feature extraction, Deep learning, Computer architecture,
image classification
BibRef
Vidal Pino, O.[Omar],
Nascimento, E.R.[Erickson R.],
Campos, M.F.M.[Mario F.M.],
Introducing the structural bases of typicality effects in deep
learning,
IVC(113), 2021, pp. 104249.
Elsevier DOI
2108
Typicality effects, Category semantic representation,
Image semantic representation, Semantic classification, Prototype theory
BibRef
Fan, B.[Bin],
Liu, H.M.[Hong-Min],
Zeng, H.[Hui],
Zhang, J.Y.[Ji-Yong],
Liu, X.[Xin],
Han, J.W.[Jun-Wei],
Deep Unsupervised Binary Descriptor Learning Through Locality
Consistency and Self Distinctiveness,
MultMed(23), 2021, pp. 2770-2781.
IEEE DOI
2109
Machine learning, Robustness, Quantization (signal), Binary codes,
Task analysis, Feature extraction, Training, Unsupervised learning,
image retrieval
BibRef
Wang, X.[Xin],
Wang, S.[Shiyi],
Ning, C.[Chen],
Zhou, H.Y.[Hui-Yu],
Enhanced Feature Pyramid Network With Deep Semantic Embedding for
Remote Sensing Scene Classification,
GeoRS(59), No. 9, September 2021, pp. 7918-7932.
IEEE DOI
2109
Feature extraction, Semantics, Spatial resolution, Convolution,
Remote sensing, Deconvolution, Task analysis, scene classification
BibRef
Miao, Y.Q.[Yun-Qi],
Lin, Z.J.[Zi-Jia],
Ma, X.[Xiao],
Ding, G.G.[Gui-Guang],
Han, J.G.[Jun-Gong],
Learning Transformation-Invariant Local Descriptors With Low-Coupling
Binary Codes,
IP(30), 2021, pp. 7554-7566.
IEEE DOI
2109
Correlation, Binary codes, Training, Visualization,
Feature extraction, Entropy, Deep learning, deep learning
BibRef
Yang, S.J.[Shi-Jie],
Li, L.[Liang],
Wang, S.H.[Shu-Hui],
Zhang, W.G.[Wei-Gang],
Huang, Q.M.[Qing-Ming],
Tian, Q.[Qi],
Graph Regularized Encoder-Decoder Networks for Image Representation
Learning,
MultMed(23), 2021, pp. 3124-3136.
IEEE DOI
2109
BibRef
Earlier: A1, A2, A3, A4, A5, Only:
A Graph Regularized Deep Neural Network for Unsupervised Image
Representation Learning,
CVPR17(7053-7061)
IEEE DOI
1711
Laplace equations, Visualization, Manifolds, Image reconstruction,
Task analysis, Decoding, Semantics, Auto-encoder, encoder-decoder,
image representation learning.
Neural networks, Robustness.
BibRef
Zhao, T.Y.[Tian-Yu],
Zhao, J.[Jian],
Zhou, W.G.[Wen-Gang],
Zhou, Y.[Yun],
Li, H.Q.[Hou-Qiang],
State Representation Learning With Adjacent State Consistency Loss
for Deep Reinforcement Learning,
MultMedMag(28), No. 3, July 2021, pp. 117-127.
IEEE DOI
2109
Training data, Task analysis, Reinforcement learning,
Feature extraction, Games, Neural networks, Deep learning,
Computer Vision
BibRef
Ma, X.[Xu],
Guo, J.[Jingda],
Sansom, A.[Andrew],
McGuire, M.[Mara],
Kalaani, A.[Andrew],
Chen, Q.[Qi],
Tang, S.[Sihai],
Yang, Q.[Qing],
Fu, S.[Song],
Spatial Pyramid Attention for Deep Convolutional Neural Networks,
MultMed(23), 2021, pp. 3048-3058.
IEEE DOI
2109
Object detection, Feature extraction, Convolutional codes,
Computer architecture, Benchmark testing, Topology, Task analysis,
structural information
BibRef
Liu, R.[Rui],
Liu, Y.[Yi],
Wang, R.[Rui],
Zhou, Y.C.[Yu-Cong],
Mutual calibration training: Training deep neural networks with noisy
labels using dual-models,
CVIU(212), 2021, pp. 103277.
Elsevier DOI
2110
Image classification, Deep neural networks, Weak supervisor
BibRef
Pestana, C.[Camilo],
Liu, W.[Wei],
Glance, D.[David],
Owens, R.[Robyn],
Mian, A.[Ajmal],
Assistive Signals for Deep Neural Network Classifiers,
LXCV21(1221-1225)
IEEE DOI
2109
Deep learning,
Perturbation methods, Optimization methods, Lighting
BibRef
Lin, Y.[Yi],
Wang, N.[Namin],
Ma, X.Q.[Xiao-Qing],
Li, Z.[Ziwei],
Bai, G.[Gang],
How Does DCNN Make Decisions ?,
ICPR21(3342-3349)
IEEE DOI
2105
Training, Convolution, Semantics, Decision making,
Computer architecture, Visual systems, Feature extraction,
Deep Convolutional Neural Network
BibRef
Mastan, I.D.[Indra Deep],
Raman, S.[Shanmuganathan],
DeepCFL: Deep Contextual Features Learning from a Single Image,
WACV21(2896-2905)
IEEE DOI
2106
Training, Image synthesis, Semantics,
Training data, Image restoration
BibRef
Kobayashi, T.[Takumi],
Phase-wise Parameter Aggregation For Improving SGD Optimization,
WACV21(2624-2633)
IEEE DOI
2106
Stochastic gradient descent. Applied to deep net training.
Training, Optimization methods,
Convolutional neural networks, Task analysis, Surface treatment
BibRef
Kundu, S.[Souvik],
Datta, G.[Gourav],
Pedram, M.[Massoud],
Beerel, P.A.[Peter A.],
Spike-Thrift: Towards Energy-Efficient Deep Spiking Neural Networks
by Limiting Spiking Activity via Attention-Guided Compression,
WACV21(3952-3961)
IEEE DOI
2106
Training, Machine learning algorithms, Limiting, Firing,
Computational modeling, Artificial neural networks, Machine learning
BibRef
Ding, Y.[Yifan],
Wang, L.[Liqiang],
Gong, B.[Boqing],
Analyzing Deep Neural Network's Transferability via Fréchet Distance,
WACV21(3931-3940)
IEEE DOI
2106
Measurement, Degradation, Training, Correlation,
Transfer learning, Neural networks
BibRef
Jamadandi, A.[Adarsh],
Tigadoli, R.[Rishabh],
Tabib, R.[Ramesh],
Mudenagudi, U.[Uma],
Probabilistic Word Embeddings in Kinematic Space,
ICPR21(8759-8765)
IEEE DOI
2105
Geometry, Uncertainty, Computational modeling, Kinematics,
Transforms, Aerospace electronics, Tools
BibRef
Ding, P.L.K.[Pak Lun Kevin],
Martin, S.[Sarah],
Li, B.X.[Bao-Xin],
Improving Batch Normalization with Skewness Reduction for Deep Neural
Networks,
ICPR21(7165-7172)
IEEE DOI
2105
Training, Neural networks, Transforms, Optimization
BibRef
Shiran, G.[Guy],
Weinshall, D.[Daphna],
Multi-Modal Deep Clustering: Unsupervised Partitioning of Images,
ICPR21(4728-4735)
IEEE DOI
2105
Deep learning, Neural networks, Image representation,
Benchmark testing, Task analysis, Gaussian mixture model
BibRef
Liao, W.H.[Wen-Hung],
Huang, Y.T.[Yen-Ting],
Investigation of DNN Model Robustness Using Heterogeneous Datasets,
ICPR21(4393-4397)
IEEE DOI
2105
Training, Deep learning, Image coding, Neural networks,
Training data, Feature extraction, Data models
BibRef
Nakada, M.[Masaki],
Chen, H.[Honglin],
Lakshmipathy, A.[Arjun],
Terzopoulos, D.[Demetri],
Locally-Connected, Irregular Deep Neural Networks for Biomimetic
Active Vision in a Simulated Human,
ICPR21(4465-4472)
IEEE DOI
2105
Visualization, Solid modeling,
Biological system modeling, Training data, Prototypes, Retina
BibRef
Berg, A.[Axel],
Oskarsson, M.[Magnus],
O'Connor, M.[Mark],
Deep Ordinal Regression with Label Diversity,
ICPR21(2740-2747)
IEEE DOI
2105
Training, Learning systems, Neural networks, Pose estimation,
Diversity methods, Predictive models, Search problems
BibRef
Takenaga, S.[Shintaro],
Watanabe, S.[Shuhei],
Nomura, M.[Masahiro],
Ozaki, Y.[Yoshihiko],
Onishi, M.[Masaki],
Habe, H.[Hitoshi],
Evaluating Initialization of Nelder-Mead Method for Hyperparameter
Optimization in Deep Learning,
ICPR21(3372-3379)
IEEE DOI
2105
Deep learning, Shape, Market research, Optimization
BibRef
Georgiou, T.[Theodoros],
Schmitt, S.[Sebastian],
Bäck, T.[Thomas],
Pu, N.[Nan],
Chen, W.[Wei],
Lew, M.[Michael],
Comparison of deep learning and hand crafted features for mining
simulation data,
ICPR21(1-8)
IEEE DOI
2105
Deep learning, Solid modeling, Dictionaries, Computational modeling,
Detectors
BibRef
Hansen, P.[Patrick],
Vilkin, A.[Alexey],
Krustalev, Y.[Yury],
Imber, J.[James],
Talagala, D.[Dumidu],
Hanwell, D.[David],
Mattina, M.[Matthew],
Whatmough, P.N.[Paul N.],
ISP4ML: The Role of Image Signal Processing in Efficient Deep
Learning Vision Systems,
ICPR21(2438-2445)
IEEE DOI
2105
Training, Machine vision, Pipelines, Memory management,
Signal processing, Market research, Software
BibRef
Poyser, M.[Matt],
Atapour-Abarghouei, A.[Amir],
Breckon, T.P.[Toby P.],
On the Impact of Lossy Image and Video Compression on the Performance
of Deep Convolutional Neural Network Architectures,
ICPR21(2830-2837)
IEEE DOI
2105
Performance evaluation, Image segmentation, Image coding,
Pose estimation, Transform coding, Network architecture, Video compression
BibRef
Brigato, L.[Lorenzo],
Iocchi, L.[Luca],
A Close Look at Deep Learning with Small Data,
ICPR21(2490-2497)
IEEE DOI
2105
Training, Deep learning, Computational modeling, Pipelines,
Computer architecture, Benchmark testing, Complexity theory
BibRef
Du, S.Y.[Shuai-Yuan],
Hong, C.Y.[Chao-Yi],
Pan, Z.[Zhiyu],
Feng, C.[Chen],
Cao, Z.G.[Zhi-Guo],
Parallel Network to Learn Novelty from the Known,
ICPR21(2172-2179)
IEEE DOI
2105
Training, Aggregates, Training data, Benchmark testing, Data models,
Anomaly detection
BibRef
Meng, L.H.[Ling-Heng],
Gorbet, R.[Rob],
Kulic, D.[Dana],
The Effect of Multi-step Methods on Overestimation in Deep
Reinforcement Learning,
ICPR21(347-353)
IEEE DOI
2105
Computational modeling, Neural networks, Buildings,
Reinforcement learning, Approximation error
BibRef
Georgiou, T.[Theodoros],
Schmitt, S.[Sebastian],
Bäck, T.[Thomas],
Chen, W.[Wei],
Lew, M.[Michael],
Norm Loss: An efficient yet effective regularization method for deep
neural networks,
ICPR21(8812-8818)
IEEE DOI
2105
Training, Manifolds, Neural networks, Computer architecture,
Benchmark testing, Pattern recognition, Computational efficiency
BibRef
Jie, R.L.[Ren-Long],
Gao, J.B.[Jun-Bin],
Vasnev, A.[Andrey],
Tran, M.N.[Minh-Ngoc],
Regularized Flexible Activation Function Combination for Deep Neural
Networks,
ICPR21(2001-2008)
IEEE DOI
2105
Convolutional codes, Image coding, Time series analysis,
Neural networks, Stability criteria, Predictive models, Pattern recognition
BibRef
Goncalves do Santos, C.F.[Claudio Filipi],
Colombo, D.[Danilo],
Roder, M.[Mateus],
Papa, J.P.[João Paulo],
MaxDropout: Deep Neural Network Regularization Based on Maximum
Output Values,
ICPR21(2671-2676)
IEEE DOI
2105
Deep learning, Neurons, Turning, Pattern recognition,
Convolutional neural networks, Biological neural networks, Image classification
BibRef
Deng, X.[Xiang],
Zhang, Z.F.M.[Zhong-Fei Mark],
Is the Meta-Learning Idea Able to Improve the Generalization of Deep
Neural Networks on the Standard Supervised Learning?,
ICPR21(150-157)
IEEE DOI
2105
Training, Supervised learning, Neural networks, Benchmark testing,
Linear programming, Pattern recognition, Computational efficiency
BibRef
Beeching, E.[Edward],
Debangoye, J.[Jilles],
Simonin, O.[Oliver],
Wolf, C.[Christian],
Deep Reinforcement Learning on a Budget:
3D Control and Reasoning Without a Supercomputer,
ICPR21(158-165)
IEEE DOI
2105
Training, Solid modeling,
Reinforcement learning, Benchmark testing, Cognition, Supercomputers
BibRef
Rodríguez-Rodríguez, J.A.[José A.],
Molina-Cabello, M.A.[Miguel A.],
Benítez-Rochel, R.[Rafaela],
López-Rubio, E.[Ezequiel],
The Effect of Noise and Brightness on Convolutional Deep Neural
Networks,
MOI2QDN20(639-654).
Springer DOI
2103
BibRef
Thomas, H.,
Rotation-Invariant Point Convolution With Multiple Equivariant
Alignments.,
3DV20(504-513)
IEEE DOI
2102
Convolution, Kernel, Standards,
Deep learning, Shape, Neural networks, Deep Learning, Point Clouds,
Convolution
BibRef
Li, X.L.[Xi-Lai],
Sun, W.[Wei],
Wu, T.F.[Tian-Fu],
Attentive Normalization,
ECCV20(XVII:70-87).
Springer DOI
2011
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)
IEEE DOI
2011
Feature extraction, Task analysis, Computer architecture,
Object detection, Charge coupled devices, Training,
multi-scale
BibRef
Oyedotun, O.K.[Oyebade K.],
Shabayek, A.E.[Abd El_Rahman],
Aouada, D.[Djamila],
Ottersten, B.[Björn],
Revisiting the Training of Very Deep Neural Networks without Skip
Connections,
ICPR21(2724-2731)
IEEE DOI
2105
BibRef
Earlier:
Going Deeper With Neural Networks Without Skip Connections,
ICIP20(1756-1760)
IEEE DOI
2011
Training, Neural networks, Explosions, Pattern recognition,
Task analysis, Optimization.
Error analysis, Optimization,
Computational modeling, Deep neural network, PlainNet, classification
BibRef
You, J.,
Korhonen, J.,
Attention Boosted Deep Networks For Video Classification,
ICIP20(1761-1765)
IEEE DOI
2011
Feature extraction, Video sequences,
Computer architecture,
video classification
BibRef
Zhang, X.[Xiao],
Zhao, R.[Rui],
Qiao, Y.[Yu],
Li, H.S.[Hong-Sheng],
RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis
Function Softmax,
ECCV20(XXVI:296-311).
Springer DOI
2011
Code, RBF.
WWW Link.
BibRef
Chen, Y.P.[Yin-Peng],
Dai, X.Y.[Xi-Yang],
Liu, M.C.[Meng-Chen],
Chen, D.D.[Dong-Dong],
Yuan, L.[Lu],
Liu, Z.C.[Zi-Cheng],
Dynamic ReLU,
ECCV20(XIX:351-367).
Springer DOI
2011
Rectified linear units
BibRef
Reimers, C.[Christian],
Runge, J.[Jakob],
Denzler, J.[Joachim],
Determining the Relevance of Features for Deep Neural Networks,
ECCV20(XXVI:330-346).
Springer DOI
2011
BibRef
Zhao, J.J.[Jun-Jie],
Lu, D.H.[Dong-Huan],
Ma, K.[Kai],
Zhang, Y.[Yu],
Zheng, Y.F.[Ye-Feng],
Deep Image Clustering with Category-style Representation,
ECCV20(XIV:54-70).
Springer DOI
2011
WWW Link.
BibRef
Chen, D.D.[Dong-Dong],
Davies, M.E.[Mike E.],
Deep Decomposition Learning for Inverse Imaging Problems,
ECCV20(XXVIII:510-526).
Springer DOI
2011
Code, DNN.
WWW Link.
BibRef
Huang, L.[Lei],
Qin, J.[Jie],
Liu, L.[Li],
Zhu, F.[Fan],
Shao, L.[Ling],
Layer-wise Conditioning Analysis in Exploring the Learning Dynamics of
DNNs,
ECCV20(II:384-401).
Springer DOI
2011
BibRef
Li, D.[Duo],
Chen, Q.F.[Qi-Feng],
Deep Reinforced Attention Learning for Quality-Aware Visual Recognition,
ECCV20(XVI: 493-509).
Springer DOI
2010
BibRef
Yong, H.W.[Hong-Wei],
Huang, J.Q.[Jian-Qiang],
Meng, D.Y.[De-Yu],
Hua, X.S.[Xian-Sheng],
Zhang, L.[Lei],
Momentum Batch Normalization for Deep Learning with Small Batch Size,
ECCV20(XII: 224-240).
Springer DOI
2010
BibRef
Gustafsson, F.K.,
Danelljan, M.,
Schon, T.B.,
Evaluating Scalable Bayesian Deep Learning Methods for Robust
Computer Vision,
SAIAD20(1289-1298)
IEEE DOI
2008
Uncertainty, Task analysis, Estimation, Predictive models,
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)
IEEE DOI
2008
To learn shapes.
Shape, Robustness, Decoding,
Solid modeling, Training, Neural networks
BibRef
Qian, Q.[Qi],
Hu, J.H.[Ju-Hua],
Li, H.[Hao],
Hierarchically Robust Representation Learning,
CVPR20(7334-7342)
IEEE DOI
2008
Robustness, Task analysis, Feature extraction, Optimization,
Benchmark testing, Training, Data models
BibRef
Huang, L.[Lei],
Liu, L.[Li],
Zhu, F.[Fan],
Wan, D.W.[Di-Wen],
Yuan, Z.H.[Ze-Huan],
Li, B.[Bo],
Shao, L.[Ling],
Controllable Orthogonalization in Training DNNs,
CVPR20(6428-6437)
IEEE DOI
2008
Training, Matrix decomposition, Convergence, Neural networks,
Eigenvalues and eigenfunctions, Covariance matrices, Jacobian matrices
BibRef
Zhang, R.,
Peng, Z.,
Wu, L.,
Li, Z.,
Luo, P.,
Exemplar Normalization for Learning Deep Representation,
CVPR20(12723-12732)
IEEE DOI
2008
Task analysis, Training, Tensile stress, Standards, Switches,
Noise measurement, Benchmark testing
BibRef
Jaiswal, M.S.,
Kang, B.,
Lee, J.,
Cho, M.,
MUTE: Inter-class Ambiguity Driven Multi-hot Target Encoding for Deep
Neural Network Design,
DeepVision20(3254-3263)
IEEE DOI
2008
Encoding, Neural networks, Hamming distance, Noise measurement,
Semantics, Computational modeling, Training
BibRef
Le, E.,
Kokkinos, I.,
Mitra, N.J.,
Going Deeper With Lean Point Networks,
CVPR20(9500-9509)
IEEE DOI
2008
Convolution, Memory management,
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)
IEEE DOI
2004
affine transforms, feature extraction, image classification,
learning (artificial intelligence), multilayer perceptrons,
Deep learning
BibRef
Durand, T.[Thibaut],
Mehrasa, N.[Nazanin],
Mori, G.[Greg],
Learning a Deep ConvNet for Multi-Label Classification With Partial
Labels,
CVPR19(647-657).
IEEE DOI
2002
BibRef
Hossain, M.T.[Md Tahmid],
Teng, S.W.[Shyh Wei],
Zhang, D.S.[Deng-Sheng],
Lim, S.[Suryani],
Lu, G.J.[Guo-Jun],
Distortion Robust Image Classification Using Deep Convolutional
Neural Network with Discrete Cosine Transform,
ICIP19(659-663)
IEEE DOI
1910
CNN, DCT, Dropout, Distortion, VGG16
BibRef
Arroyo, R.[Roberto],
Tovar, J.[Javier],
Delgado, F.J.[Francisco J.],
Almazán, E.J.[Emilio J.],
Serrador, D.G.[Diego G.],
Hurtado, A.[Antonio],
Deep Learning of Visual and Textual Data for Region Detection Applied
to Item Coding,
IbPRIA19(I:329-341).
Springer DOI
1910
Using text on the image.
BibRef
Onchis, D.M.[Darian M.],
Istin, C.[Codruta],
Real, P.[Pedro],
Refined Deep Learning for Digital Objects Recognition via Betti
Invariants,
CAIP19(I:613-621).
Springer DOI
1909
BibRef
Lan, X.[Xu],
Zhu, X.T.[Xia-Tian],
Gong, S.G.[Shao-Gang],
Self-Referenced Deep Learning,
ACCV18(II:284-300).
Springer DOI
1906
BibRef
Pai, G.,
Talmon, R.,
Bronstein, A.,
Kimmel, R.,
DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic
Sampling,
WACV19(819-828)
IEEE DOI
1904
computational geometry, differential geometry, neural nets,
sampling methods, unsupervised learning, DIMAL,
Interpolation
BibRef
Hinterstoisser, S.[Stefan],
Lepetit, V.[Vincent],
Wohlhart, P.[Paul],
Konolige, K.[Kurt],
On Pre-trained Image Features and Synthetic Images for Deep Learning,
4DPose18(I:682-697).
Springer DOI
1905
BibRef
Haeusser, P.[Philip],
Plapp, J.[Johannes],
Golkov, V.[Vladimir],
Aljalbout, E.[Elie],
Cremers, D.[Daniel],
Associative Deep Clustering:
Training a Classification Network with No Labels,
GCPR18(18-32).
Springer DOI
1905
BibRef
Zhang, H.[Huan],
Shi, H.[Hong],
Wang, W.W.[Wen-Wu],
Cascade Deep Networks for Sparse Linear Inverse Problems,
ICPR18(812-817)
IEEE DOI
1812
Inverse problems, Linear programming, Image resolution,
Signal resolution, Convergence, Time complexity
BibRef
Duan, Y.Q.[Yue-Qi],
Wang, Z.W.[Zi-Wei],
Lu, J.W.[Ji-Wen],
Lin, X.D.[Xu-Dong],
Zhou, J.[Jie],
GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning,
CVPR18(8270-8279)
IEEE DOI
1812
Reliability, Binary codes, Computer vision, Linear programming,
Training, Mutual information
BibRef
Keller, M.[Michel],
Chen, Z.[Zetao],
Maffra, F.[Fabiola],
Schmuck, P.[Patrik],
Chli, M.[Margarita],
Learning Deep Descriptors with Scale-Aware Triplet Networks,
CVPR18(2762-2770)
IEEE DOI
1812
Computer vision, Training, Neural networks, Task analysis, Feature extraction
BibRef
Goh, C.K.,
Liu, Y.,
Kong, A.W.K.,
A Constrained Deep Neural Network for Ordinal Regression,
CVPR18(831-839)
IEEE DOI
1812
Optimization, Feature extraction, Neural networks,
Support vector machines, Training, Measurement
BibRef
Yang, Y.Q.[Yao-Qing],
Feng, C.[Chen],
Shen, Y.[Yiru],
Tian, D.[Dong],
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation,
CVPR18(206-215)
IEEE DOI
1812
Decoding, Image reconstruction, Surface reconstruction, Neural networks
BibRef
Caron, M.[Mathilde],
Bojanowski, P.[Piotr],
Joulin, A.[Armand],
Douze, M.[Matthijs],
Deep Clustering for Unsupervised Learning of Visual Features,
ECCV18(XIV: 139-156).
Springer DOI
1810
BibRef
Xu, Y.F.[Yi-Fan],
Fan, T.Q.[Tian-Qi],
Xu, M.Y.[Ming-Ye],
Zeng, L.[Long],
Qiao, Y.[Yu],
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional
Filters,
ECCV18(VIII: 90-105).
Springer DOI
1810
BibRef
Huang, Z.[Zehao],
Wang, N.[Naiyan],
Data-Driven Sparse Structure Selection for Deep Neural Networks,
ECCV18(XVI: 317-334).
Springer DOI
1810
BibRef
Wang, Q.[Qiang],
Xu, J.Q.[Jia-Qing],
Li, R.C.[Rong-Chun],
Qiao, P.[Peng],
Yang, K.[Ke],
Li, S.J.[Shi-Jie],
Dou, Y.[Yong],
Deep Image Clustering Using Convolutional Autoencoder Embedding with
Inception-Like Block,
ICIP18(2356-2360)
IEEE DOI
1809
Convolutional codes, Convolution, Clustering algorithms,
Image reconstruction, Decoding, Clustering methods, Task analysis,
Kullback-Leibler divergence
BibRef
Alqahtani, A.,
Xie, X.,
Deng, J.,
Jones, M.W.,
Learning Discriminatory Deep Clustering Models,
CAIP19(I:224-233).
Springer DOI
1909
BibRef
And:
A Deep Convolutional Auto-Encoder with Embedded Clustering,
ICIP18(4058-4062)
IEEE DOI
1809
Image reconstruction, Training, Feature extraction,
Linear programming, Task analysis, Convolution, Cost function,
Embedded Clustering
BibRef
Chun, Y.,
Fessler, J.A.,
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for
Iterative Image Recovery,
IVMSP18(1-5)
IEEE DOI
1809
Training, Convolution, Iterative decoding, Thresholding (Imaging),
Imaging, Decoding, Encoding
BibRef
Smith, K.E.[Kaleb E.],
Williams, P.[Phillip],
Chaiya, T.[Tatsanee],
Ble, M.[Max],
Deep Convolutional-Shepard Interpolation Neural Networks for Image
Classification Tasks,
ICIAR18(185-192).
Springer DOI
1807
BibRef
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
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
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
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
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
Nguyen, K.[Kien],
Fookes, C.[Clinton],
Sridharan, S.[Sridha],
Deep Context Modeling for Semantic Segmentation,
WACV17(56-63)
IEEE DOI
1609
BibRef
Earlier:
Deeper and wider fully convolutional network coupled with conditional
random fields for scene labeling,
ICIP16(1344-1348)
IEEE DOI
1610
BibRef
Earlier:
Improving deep convolutional neural networks with unsupervised
feature learning,
ICIP15(2270-2274)
IEEE DOI
1512
Feature extraction, Graphical models, Image segmentation, Kernel,
Labeling, Neural networks, Semantics, context modeling,
scene parsing, scene understanding, semantic segmentation.
Computational modeling.
Convolutional Neural Network
BibRef
Talathi, S.S.[Sachin S.],
Hyper-parameter optimization of deep convolutional networks for
object recognition,
ICIP15(3982-3986)
IEEE DOI
1512
deep convolution networks
BibRef
Yamashita, T.[Takayoshi],
Tanaka, M.[Masayuki],
Yamauchi, Y.[Yuji],
Fujiyoshi, H.[Hironobu],
SWAP-NODE:
A regularization approach for deep convolutional neural networks,
ICIP15(2475-2479)
IEEE DOI
1512
deep learning; dropout; regularization; swap-node
BibRef
Afzal, M.Z.[Muhammad Zeshan],
Capobianco, S.[Samuele],
Malik, M.I.[Muhammad Imran],
Marinai, S.[Simone],
Breuel, T.M.[Thomas M.],
Dengel, A.[Andreas],
Liwicki, M.[Marcus],
Deepdocclassifier:
Document classification with deep Convolutional Neural Network,
ICDAR15(1111-1115)
IEEE DOI
1511
Convolutional Neural Network;Deep CNN;Document Image Classification
BibRef
Christodoulidis, S.[Stergios],
Anthimopoulos, M.[Marios],
Mougiakakou, S.[Stavroula],
Food Recognition for Dietary Assessment Using Deep Convolutional Neural
Networks,
MADiMa15(458-465).
Springer DOI
1511
BibRef
Li, Y.[Yao],
Liu, L.Q.[Ling-Qiao],
Shen, C.H.[Chun-Hua],
van den Hengel, A.J.[Anton J.],
Mid-level deep pattern mining,
CVPR15(971-980)
IEEE DOI
1510
Convolutional Neural Networks.
BibRef
Wang, M.[Min],
Liu, B.Y.[Bao-Yuan],
Foroosh, H.[Hassan],
Look-Up Table Unit Activation Function for Deep Convolutional Neural
Networks,
WACV18(1225-1233)
IEEE DOI
1806
BibRef
Earlier:
Factorized Convolutional Neural Networks,
Matrix-Tensor17(545-553)
IEEE DOI
1802
Gaussian processes, convolution, data structures,
feedforward neural nets, image recognition, interpolation,
Training.
Complexity theory, Convolution, Kernel, Standards,
BibRef
Amthor, M.[Manuel],
Rodner, E.[Erik],
Denzler, J.[Joachim],
Impatient DNNs: Deep Neural Networks with Dynamic Time Budgets,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Simon, M.[Marcel],
Rodner, E.[Erik],
Neural Activation Constellations: Unsupervised Part Model Discovery
with Convolutional Networks,
ICCV15(1143-1151)
IEEE DOI
1602
Birds
BibRef
Rodner, E.[Erik],
Simon, M.[Marcel],
Fisher, R.[Robert],
Denzler, J.[Joachim],
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of
Convolutional Neural Networks Approaches,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Denzler, J.[Joachim],
Rodner, E.[Erik],
Simon, M.[Marcel],
Convolutional Neural Networks as a Computational Model for the
Underlying Processes of Aesthetics Perception,
CVAA16(I: 871-887).
Springer DOI
1611
BibRef
Simon, M.[Marcel],
Rodner, E.[Erik],
Denzler, J.[Joachim],
Part Detector Discovery in Deep Convolutional Neural Networks,
ACCV14(II: 162-177).
Springer DOI
1504
BibRef
Ng, J.Y.H.[Joe Yue-Hei],
Hausknecht, M.[Matthew],
Vijayanarasimhan, S.[Sudheendra],
Vinyals, O.[Oriol],
Monga, R.[Rajat],
Toderici, G.[George],
Beyond short snippets: Deep networks for video classification,
CVPR15(4694-4702)
IEEE DOI
1510
BibRef
Chen, G.B.[Guo-Bin],
Han, T.X.[Tony X.],
He, Z.H.[Zhi-Hai],
Kays, R.[Roland],
Forrester, T.[Tavis],
Deep convolutional neural network based species recognition for wild
animal monitoring,
ICIP14(858-862)
IEEE DOI
1502
Birds
BibRef
Hafemann, L.G.[Luiz G.],
Oliveira, L.S.[Luiz S.],
Cavalin, P.[Paulo],
Forest Species Recognition Using Deep Convolutional Neural Networks,
ICPR14(1103-1107)
IEEE DOI
1412
Accuracy
BibRef
Gatta, C.[Carlo],
Romero, A.[Adriana],
van de Veijer, J.[Joost],
Unrolling Loopy Top-Down Semantic Feedback in Convolutional Deep
Networks,
DeepLearn14(504-511)
IEEE DOI
1409
BibRef
Yin, X.C.[Xu-Cheng],
Yang, C.[Chun],
Pei, W.Y.[Wei-Yi],
Hao, H.W.[Hong-Wei],
Shallow Classification or Deep Learning: An Experimental Study,
ICPR14(1904-1909)
IEEE DOI
1412
Character recognition
BibRef
Kekec, T.[Taygun],
Emonet, R.[Remi],
Fromont, E.[Elisa],
Tremeau, A.[Alain],
Wolf, C.[Christian],
Contextually Constrained Deep Networks for Scene Labeling,
BMVC14(xx-yy).
HTML Version.
1410
BibRef
Zhong, S.H.[Sheng-Hua],
Liu, Y.[Yan],
Chung, F.L.[Fu-Lai],
Wu, G.S.[Gang-Shan],
Semiconducting bilinear deep learning for incomplete image recognition,
ICMR12(32).
DOI Link
1301
semiconducting bilinear deep belief networks (SBDBN)
human's visual cortex.
BibRef
Ciregan, D.[Dan],
Meier, U.[Ueli],
Schmidhuber, J.[Jurgen],
Multi-column deep neural networks for image classification,
CVPR12(3642-3649).
IEEE DOI
1208
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Loss Functions, Deep Learning, Neural Netowrks .