14.5.8.4.3 Residual Neural Networks, ResNet

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, 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

Xie, F.D.[Fu-Ding], Gao, Q.S.[Quan-Shan], Jin, C.[Cui], Zhao, F.X.[Feng-Xia],
Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Gao, Q.S.[Qi-Shuo], 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

Zhong, Z.L.[Zi-Long], Li, J.[Jonathan], Luo, Z.M.[Zhi-Ming], Chapman, M.[Michael],
Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework,
GeoRS(56), No. 2, February 2018, pp. 847-858.
IEEE DOI 1802
Feature extraction, Hyperspectral imaging, Machine learning, Robustness, Testing, Training, 3-D deep learning, spectral-spatial residual network (SSRN) BibRef

Meng, Z.[Zhe], Li, L.L.[Ling-Ling], Jiao, L.C.[Li-Cheng], Feng, Z.X.[Zhi-Xi], Tang, X.[Xu], Liang, M.M.[Miao-Miao],
Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911

See also Fast Deep Perception Network for Remote Sensing Scene Classification, A. BibRef

Zhao, F.[Feng], Zhang, J.J.[Jun-Jie], Meng, Z.[Zhe], Liu, H.Q.[Han-Qiang],
Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Meng, Z.[Zhe], Li, L.L.[Ling-Ling], Tang, X.[Xu], Feng, Z.X.[Zhi-Xi], Jiao, L.C.[Li-Cheng], Liang, M.M.[Miao-Miao],
Multipath Residual Network for Spectral-Spatial Hyperspectral Image Classification,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Feng, J.[Jie], Wang, L.[Lin], Yu, H.[Haipeng], Jiao, L.C.[Li-Cheng], Zhang, X.R.[Xiang-Rong],
Divide-and-Conquer Dual-Architecture Convolutional Neural Network for Classification of Hyperspectral Images,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Li, G.[Ge], Li, L.L.[Ling-Ling], Zhu, H.[Hao], Liu, X.[Xu], Jiao, L.C.[Li-Cheng],
Adaptive Multiscale Deep Fusion Residual Network for Remote Sensing Image Classification,
GeoRS(57), No. 11, November 2019, pp. 8506-8521.
IEEE DOI 1911
Feature extraction, Training, Semantics, Image segmentation, Adaptive systems, Hyperspectral sensors, Deep learning (DL), remote sensing BibRef

Wang, L.[Li], Peng, J.T.[Jiang-Tao], Sun, W.W.[Wei-Wei],
Spatial-Spectral Squeeze-and-Excitation Residual Network for Hyperspectral Image Classification,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
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

Li, G.Q.[Guo-Qiang], Chen, W.H.[Wen-Hua], Mu, C.[Chao],
Residual-wider convolutional neural network for image recognition,
IET-IPR(14), No. 16, 19 December 2020, pp. 4385-4391.
DOI Link 2103
BibRef

Roy, S.K., Chatterjee, S., Bhattacharyya, S., Chaudhuri, B.B., Platoš, J.,
Lightweight Spectral-Spatial Squeeze-and-Excitation Residual Bag-of-Features Learning for Hyperspectral Classification,
GeoRS(58), No. 8, August 2020, pp. 5277-5290.
IEEE DOI 2007
Feature extraction, Hyperspectral imaging, Principal component analysis, Data mining, Training, residual network (ResNet) BibRef

Khotimah, W.N.[Wijayanti Nurul], Bennamoun, M.[Mohammed], Boussaid, F.[Farid], Sohel, F.[Ferdous], Edwards, D.[David],
A High-Performance Spectral-Spatial Residual Network for Hyperspectral Image Classification with Small Training Data,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Zeng, Y.L.[Yi-Liang], Ritz, C.[Christian], Zhao, J.H.[Jia-Hong], Lan, J.H.[Jin-Hui],
Attention-Based Residual Network with Scattering Transform Features for Hyperspectral Unmixing with Limited Training Samples,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Mahmood, A.[Ammar], Bennamoun, M.[Mohammed], An, S.[Senjian], Sohel, F.A.[Ferdous A.], Boussaid, F.[Farid],
ResFeats: Residual network based features for underwater image classification,
IVC(93), 2020, pp. 103811.
Elsevier DOI 2001
BibRef
Earlier: A1, A2, A3, A4, Only:
ResFeats: Residual network based features for image classification,
ICIP17(1597-1601)
IEEE DOI 1803
Deep learning, Residual networks, Deep features, Image classification, Underwater image classification. Convolution, Dimensionality reduction, Feature extraction, Image representation, Task analysis, Testing, Training, scene classification BibRef

Gao, H., Yang, Y., Li, C., Gao, L., Zhang, B.,
Multiscale Residual Network With Mixed Depthwise Convolution for Hyperspectral Image Classification,
GeoRS(59), No. 4, April 2021, pp. 3396-3408.
IEEE DOI 2104
Feature extraction, Convolution, Training, Hyperspectral imaging, Data mining, Convolutional neural networks, multiscale residual block (MRB) BibRef

Chen, W.J.[Wen-Jing], Zheng, X.T.[Xiang-Tao], Lu, X.Q.[Xiao-Qiang],
Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104

See also Spectral-Spatial Attention Network for Hyperspectral Image Classification. BibRef

Benz, P.[Philipp], Zhang, C.[Chaoning], Karjauv, A.[Adil], Kweon, I.S.[In So],
Revisiting Batch Normalization for Improving Corruption Robustness,
WACV21(494-503)
IEEE DOI 2106
Adaptation models, Perturbation methods, Computer architecture, Benchmark testing, Market research BibRef

Duta, I.C.[Ionut Cosmin], Liu, L.[Li], Zhu, F.[Fan], Shao, L.[Ling],
Improved Residual Networks for Image and Video Recognition,
ICPR21(9415-9422)
IEEE DOI 2105
Training, Image recognition, Object detection, Distance measurement, Complexity theory, Task analysis BibRef

Shen, Y.H.[Yun-Hang], Ji, R.R.[Rong-Rong], Wang, Y.[Yan], Chen, Z.W.[Zhi-Wei], Zheng, F.[Feng], Huang, F.Y.[Fei-Yue], Wu, Y.S.[Yun-Sheng],
Enabling Deep Residual Networks for Weakly Supervised Object Detection,
ECCV20(VIII:118-136).
Springer DOI 2011
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

Ye, K.[Keren], Kovashka, A.[Adriana], Sandler, M.[Mark], Zhu, M.L.[Meng-Long], Howard, A.[Andrew], Fornoni, M.[Marco],
Spotpatch: Parameter-efficient Transfer Learning for Mobile Object Detection,
ACCV20(VI:239-256).
Springer DOI 2103
BibRef

Sandler, M., Howard, A.[Andrew], Zhu, M.L.[Meng-Long], 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 .