Mou, L.,
Ghamisi, P.,
Zhu, X.X.,
Unsupervised Spectral-Spatial Feature Learning via Deep Residual
Conv-Deconv Network for Hyperspectral Image Classification,
GeoRS(56), No. 1, January 2018, pp. 391-406.
IEEE DOI
1801
Feature extraction, Hyperspectral imaging, Network architecture,
Support vector machines, Training, Convolutional network,
unsupervised spectral-spatial feature learning
BibRef
Mou, L.,
Bruzzone, L.,
Zhu, X.X.,
Learning Spectral-Spatial-Temporal Features via a Recurrent
Convolutional Neural Network for Change Detection in Multispectral
Imagery,
GeoRS(57), No. 2, February 2019, pp. 924-935.
IEEE DOI
1901
Feature extraction, Task analysis, Remote sensing,
Convolutional neural networks, Earth, Data mining,
recurrent convolutional neural network (ReCNN)
BibRef
Mou, L.,
Ghamisi, P.[Pedram],
Zhu, X.X.,
Deep Recurrent Neural Networks for Hyperspectral Image Classification,
GeoRS(55), No. 7, July 2017, pp. 3639-3655.
IEEE DOI
1706
BibRef
And:
Corrections:
GeoRS(56), No. 2, February 2018, pp. 1214-1215.
IEEE DOI
1802
Data models, Hyperspectral imaging, Logic gates,
Recurrent neural networks, Support vector machines,
Convolutional neural network (CNN), deep learning,
gated recurrent unit (GRU), hyperspectral image classification,
long short-term memory (LSTM), recurrent neural network (RNN)
BibRef
Hang, R.L.[Ren-Long],
Liu, Q.S.[Qing-Shan],
Hong, D.F.[Dan-Feng],
Ghamisi, P.[Pedram],
Cascaded Recurrent Neural Networks for Hyperspectral Image
Classification,
GeoRS(57), No. 8, August 2019, pp. 5384-5394.
IEEE DOI
1908
hyperspectral imaging, image classification,
learning (artificial intelligence), recurrent neural nets,
spectral-spatial feature
BibRef
Chen, Y.S.[Yu-Shi],
Jiang, H.L.[Han-Lu],
Li, C.Y.[Chun-Yang],
Jia, X.P.[Xiu-Ping],
Ghamisi, P.[Pedram],
Deep Feature Extraction and Classification of Hyperspectral Images
Based on Convolutional Neural Networks,
GeoRS(54), No. 10, October 2016, pp. 6232-6251.
IEEE DOI
1610
feature extraction
BibRef
He, X.[Xin],
Chen, Y.S.[Yu-Shi],
Ghamisi, P.[Pedram],
Heterogeneous Transfer Learning for Hyperspectral Image
Classification Based on Convolutional Neural Network,
GeoRS(58), No. 5, May 2020, pp. 3246-3263.
IEEE DOI
2005
Feature extraction, Training, Hyperspectral imaging,
Convolutional neural nets, Data models, Kernel, Classification,
transfer learning
BibRef
Duan, P.,
Kang, X.,
Li, S.,
Ghamisi, P.,
Multichannel Pulse-Coupled Neural Network-Based Hyperspectral Image
Visualization,
GeoRS(58), No. 4, April 2020, pp. 2444-2456.
IEEE DOI
2004
Visualization, Image color analysis, Hyperspectral imaging,
Neurons, Neural networks, Principal component analysis,
natural color display
BibRef
Tu, B.[Bing],
Li, N.Y.[Nan-Ying],
Fang, L.Y.[Le-Yuan],
He, D.B.[Dan-Bing],
Ghamisi, P.[Pedram],
Hyperspectral Image Classification with Multi-Scale Feature
Extraction,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link
1903
BibRef
He, N.J.[Nan-Jun],
Paoletti, M.E.[Mercedes E.],
Haut, J.M.[Juan Mario],
Fang, L.Y.[Le-Yuan],
Li, S.T.[Shu-Tao],
Plaza, A.J.[Antonio J.],
Plaza, J.[Javier],
Feature Extraction With Multiscale Covariance Maps for Hyperspectral
Image Classification,
GeoRS(57), No. 2, February 2019, pp. 755-769.
IEEE DOI
1901
Feature extraction, Hyperspectral imaging, Training,
Convolutional neural networks, Electronic mail,
multiscale covariance maps (MCMs)
BibRef
Gao, Q.S.[Qi-Shuo],
Lim, S.[Samsung],
Jia, X.P.[Xiu-Ping],
Hyperspectral Image Classification Using Convolutional Neural
Networks and Multiple Feature Learning,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link
1804
BibRef
Gao, Q.[Qishuo],
Lim, S.[Samsung],
Classification of hyperspectral images with convolutional neural
networks and probabilistic relaxation,
CVIU(188), 2019, pp. 102801.
Elsevier DOI
1910
Hyperspectral images, Image classification,
Convolutional neural networks, Probabilistic relaxation
BibRef
Boulch, A.[Alexandre],
Reducing parameter number in residual networks by sharing weights,
PRL(103), 2018, pp. 53-59.
Elsevier DOI
1802
BibRef
Zhang, K.[Ke],
Sun, M.[Miao],
Han, T.X.[Tony X.],
Yuan, X.F.[Xing-Fang],
Guo, L.[Liru],
Liu, T.[Tao],
Residual Networks of Residual Networks: Multilevel Residual Networks,
CirSysVideo(28), No. 6, June 2018, pp. 1303-1314.
IEEE DOI
1806
How to stack networks for real problems.
Computer architecture, Neural networks, Optimization,
Road transportation, Stochastic processes, Sun, Training,
stochastic depth (SD)
BibRef
Wu, Z.F.[Zi-Feng],
Shen, C.H.[Chun-Hua],
van den Hengel, A.[Anton],
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition,
PR(90), 2019, pp. 119-133.
Elsevier DOI
1903
Image classification, Semantic segmentation, Residual network
BibRef
Xu, Y.F.[Yi-Feng],
Wang, H.[Huigang],
Liu, X.[Xing],
Sun's, W.[Weitao],
An improved multi-branch residual network based on random multiplier
and adaptive cosine learning rate method,
JVCIR(59), 2019, pp. 363-370.
Elsevier DOI
1903
Image classification, Residual network, Overfitting,
Deep leaning, Batch size, Learning rate
BibRef
Dimou, A.[Anastasios],
Ataloglou, D.[Dimitrios],
Dimitropoulos, K.[Kosmas],
Alvarez, F.[Federico],
Daras, P.[Petros],
LDS-Inspired Residual Networks,
CirSysVideo(29), No. 8, August 2019, pp. 2363-2375.
IEEE DOI
1908
linear dynamical systems (LDSs).
Training, Task analysis, Stochastic processes, Object detection,
Data models, Neural networks, Integrated circuit modeling, ResNet,
object detection
BibRef
Paoletti, M.E.,
Haut, J.M.,
Plaza, J.,
Plaza, A.,
Neural Ordinary Differential Equations for Hyperspectral Image
Classification,
GeoRS(58), No. 3, March 2020, pp. 1718-1734.
IEEE DOI
2003
Neurons, Data models, Hyperspectral imaging, Feature extraction,
Data mining, Visualization, Deep learning (DL),
residual networks (ResNets)
BibRef
Zhang, L.[Linan],
Schaeffer, H.[Hayden],
Forward Stability of ResNet and Its Variants,
JMIV(62), No. 3, April 2020, pp. 328-351.
Springer DOI
2004
BibRef
Rousseau, F.[François],
Drumetz, L.[Lucas],
Fablet, R.[Ronan],
Residual Networks as Flows of Diffeomorphisms,
JMIV(62), No. 3, April 2020, pp. 365-375.
Springer DOI
2004
BibRef
Li, T.P.[Teng-Peng],
Song, H.H.[Hui-Hui],
Zhang, K.H.[Kai-Hua],
Liu, Q.S.[Qing-Shan],
Learning residual refinement network with semantic context
representation for real-time saliency object detection,
PR(105), 2020, pp. 107372.
Elsevier DOI
2006
Salient object detection, Convolutional neural networks,
Deep learning, Residual learning
BibRef
Wang, H.[Haoran],
Ji, Z.[Zhong],
Lin, Z.G.[Zhi-Gang],
Pang, Y.W.[Yan-Wei],
Li, X.L.[Xue-Long],
Stacked squeeze-and-excitation recurrent residual network for
visual-semantic matching,
PR(105), 2020, pp. 107359.
Elsevier DOI
2006
Vision and language, Cross-modal retrieval, Visual-Semantic embedding
BibRef
Zhang, S.,
Fan, Z.,
Ling, N.,
Jiang, M.,
Recursive Residual Convolutional Neural Network- Based In-Loop
Filtering for Intra Frames,
CirSysVideo(30), No. 7, July 2020, pp. 1888-1900.
IEEE DOI
2007
Encoding, Video coding, Image reconstruction, Low-pass filters,
Adaptive filters, Distortion, Convolutional neural network,
visual communications
BibRef
Taha, A.[Ahmed],
Chen, Y.T.[Yi-Ting],
Misu, T.[Teruhisa],
Shrivastava, A.[Abhinav],
Davis, L.S.[Larry S.],
Boosting Standard Classification Architectures Through a Ranking
Regularizer,
WACV20(747-755)
IEEE DOI
2006
Code, Classification.
WWW Link. Standards, Computer architecture, Head, Magnetic losses,
Magnetic separation, Visualization, Magnetic heads
BibRef
Brown, A.,
Mettes, P.,
Worring, M.,
4-Connected Shift Residual Networks,
NeruArch19(1990-1997)
IEEE DOI
2004
computational complexity, convolution, convolutional neural nets,
Gaussian processes, image colour analysis, image sampling,
Convolutional neural networks
BibRef
Liu, X.[Xing],
Suganuma, M.[Masanori],
Sun, Z.[Zhun],
Okatani, T.[Takayuki],
Dual Residual Networks Leveraging the Potential of Paired Operations
for Image Restoration,
CVPR19(7000-7009).
IEEE DOI
2002
BibRef
Zhong, X.[Xian],
Gong, O.[Oubo],
Huang, W.X.[Wen-Xin],
Li, L.[Lin],
Xia, H.X.[Hong-Xia],
Squeeze-and-Excitation Wide Residual Networks in Image Classification,
ICIP19(395-399)
IEEE DOI
1910
wide residual networks, global pooling, channel,
squeeze-and-excitation block, CIFAR
BibRef
Chen, G.,
Ding, D.,
Mukherjee, D.,
Joshi, U.,
Chen, Y.,
AV1 in-loop Filtering using a Wide-Activation Structured Residual
Network,
ICIP19(1725-1729)
IEEE DOI
1910
AV1, CNN, video compression, in-loop filter
BibRef
Zhao, X.,
Li, W.,
Zhang, Y.,
Zhang, F.,
Chang, S.,
Feng, Z.,
Residual Dilation Based Feature Pyramid Network,
ICIP19(3940-3944)
IEEE DOI
1910
Object Detection, Convolutional Neural Networks
BibRef
Li, X.,
Li, W.,
Xu, X.,
Du, Q.,
CascadeNet: Modified ResNet with Cascade Blocks,
ICPR18(483-488)
IEEE DOI
1812
Computer architecture, Convolution, Training, Testing,
Network architecture, Convolutional neural networks, Architecture
BibRef
Oyallon, E.[Edouard],
Belilovsky, E.[Eugene],
Zagoruyko, S.[Sergey],
Valko, M.[Michal],
Compressing the Input for CNNs with the First-Order Scattering
Transform,
ECCV18(IX: 305-320).
Springer DOI
1810
BibRef
Earlier: A1, A2, A3, Only:
Scaling the Scattering Transform: Deep Hybrid Networks,
ICCV17(5619-5628)
IEEE DOI
1802
Initialization of the network.
convolution, image coding, neural nets, transforms, Deep CNNs,
Deep hybrid networks, Resnet-18 architecture,
Wavelet transforms
BibRef
Zhang, X.,
Huang, S.,
Zhang, X.,
Wang, W.,
Wang, Q.,
Yang, D.,
Residual Inception: A New Module Combining Modified Residual with
Inception to Improve Network Performance,
ICIP18(3039-3043)
IEEE DOI
1809
Convolution, Kernel, Training, Fractals, Testing, Image recognition,
Machine learning, Inception module, Convolutional network,
Residual network
BibRef
Yu, X.[Xin],
Yu, Z.D.[Zhi-Ding],
Ramalingam, S.[Srikumar],
Learning Strict Identity Mappings in Deep Residual Networks,
CVPR18(4432-4440)
IEEE DOI
1812
Training, Standards, Task analysis, Optimization, Manuals,
Network architecture, Bayes methods
BibRef
Sandler, M.,
Howard, A.,
Zhu, M.,
Zhmoginov, A.,
Chen, L.,
MobileNetV2: Inverted Residuals and Linear Bottlenecks,
CVPR18(4510-4520)
IEEE DOI
1812
Manifolds, Neural networks, Computer architecture, Standards,
Computational modeling, Task analysis
BibRef
Wu, Z.,
Nagarajan, T.,
Kumar, A.,
Rennie, S.,
Davis, L.S.,
Grauman, K.,
Feris, R.,
BlockDrop: Dynamic Inference Paths in Residual Networks,
CVPR18(8817-8826)
IEEE DOI
1812
Computational modeling, Visualization, Task analysis, Training,
Computer vision, Dogs, Neural networks
BibRef
Lettry, L.,
Vanhoey, K.,
Van Gool, L.J.,
DARN: A Deep Adversarial Residual Network for Intrinsic Image
Decomposition,
WACV18(1359-1367)
IEEE DOI
1806
feedforward neural nets, image colour analysis,
learning (artificial intelligence), MPI Sintel dataset,
Training
BibRef
Wang, F.[Fei],
Jiang, M.Q.[Meng-Qing],
Qian, C.[Chen],
Yang, S.[Shuo],
Li, C.[Cheng],
Zhang, H.G.[Hong-Gang],
Wang, X.G.[Xiao-Gang],
Tang, X.[Xiaoou],
Residual Attention Network for Image Classification,
CVPR17(6450-6458)
IEEE DOI
1711
Image color analysis, Logic gates, Neural networks,
Noise measurement, Stacking, Training
BibRef
Han, D.Y.[Dong-Yoon],
Kim, J.[Jiwhan],
Kim, J.[Junmo],
Deep Pyramidal Residual Networks,
CVPR17(6307-6315)
IEEE DOI
1711
Additives, Artificial neural networks,
Feature extraction, Network, architecture
BibRef
Figurnov, M.[Michael],
Collins, M.D.[Maxwell D.],
Zhu, Y.K.[Yu-Kun],
Zhang, L.[Li],
Huang, J.[Jonathan],
Vetrov, D.[Dmitry],
Salakhutdinov, R.[Ruslan],
Spatially Adaptive Computation Time for Residual Networks,
CVPR17(1790-1799)
IEEE DOI
1711
Adaptation models, Computational modeling, Computer architecture,
Feature extraction, Image segmentation, Object detection
BibRef
Xie, S.,
Girshick, R.,
Dollár, P.,
Tu, Z.,
He, K.,
Aggregated Residual Transformations for Deep Neural Networks,
CVPR17(5987-5995)
IEEE DOI
1711
Complexity theory, Computer architecture, Network topology,
Neural networks, Neurons, Topology
BibRef
Yu, F.[Fisher],
Koltun, V.[Vladlen],
Funkhouser, T.[Thomas],
Dilated Residual Networks,
CVPR17(636-644)
IEEE DOI
1711
Convolution, Image segmentation, Semantics, Spatial resolution, Training
BibRef
Liu, Y.[Yu],
Guo, Y.M.[Yan-Ming],
Bakker, E.M.,
Lew, M.S.[Michael S.],
Learning a Recurrent Residual Fusion Network for Multimodal Matching,
ICCV17(4127-4136)
IEEE DOI
1802
image matching, image representation,
learning (artificial intelligence), text analysis, RRF,
Visualization
BibRef
Mercier, J.P.[Jean-Philippe],
Trottier, L.[Ludovic],
Giguère, P.[Philippe],
Chaib-Draa, B.[Brahim],
Deep Object Ranking for Template Matching,
WACV17(734-742)
IEEE DOI
1609
Machine learning, Neural networks, Object detection, Robustness,
Service robots,
BibRef
Trottier, L.[Ludovic],
Giguère, P.[Philippe],
Chaib-Draa, B.[Brahim],
Convolutional Residual Network for Grasp Localization,
CRV17(168-175)
IEEE DOI
1804
BibRef
And:
Sparse Dictionary Learning for Identifying Grasp Locations,
WACV17(871-879)
IEEE DOI
1609
feedforward neural nets, learning (artificial intelligence),
manipulators, robot vision, localization.
Dictionaries, Feature extraction, Grasping, Optimization, Standards, Training
BibRef
Wang, Z.[Ziqin],
Jiang, P.[Peilin],
Wang, F.[Fei],
Dense Residual Pyramid Networks for Salient Object Detection,
DeepVisual16(III: 606-621).
Springer DOI
1704
BibRef
Zagoruyko, S.[Sergey],
Komodakis, N.[Nikos],
Wide Residual Networks,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Guo, J.,
Gould, S.,
Depth Dropout: Efficient Training of Residual Convolutional Neural
Networks,
DICTA16(1-7)
IEEE DOI
1701
Biological neural networks
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Networks for Shapes and Complex Features .