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
Tu, B.[Bing],
Zhou, C.[Chengle],
Liao, X.L.[Xiao-Long],
Li, Q.M.[Qian-Ming],
Peng, Y.[Yishu],
Feature Extraction via 3-D Block Characteristics Sharing for
Hyperspectral Image Classification,
GeoRS(59), No. 12, December 2021, pp. 10503-10518.
IEEE DOI
2112
Feature extraction, Hyperspectral imaging,
Support vector machines, Image segmentation,
superpixel segmentation
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
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
See also Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification.
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
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
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
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
Zaeemzadeh, A.[Alireza],
Rahnavard, N.[Nazanin],
Shah, M.[Mubarak],
Norm-Preservation: Why Residual Networks Can Become Extremely Deep?,
PAMI(43), No. 11, November 2021, pp. 3980-3990.
IEEE DOI
2110
Optimization, Training, Residual neural networks, Convolution,
Numerical stability, Computer architecture, Residual networks,
spectral regularization
BibRef
Malladi, S.P.K.[Sai Phani Kumar],
Mukhopadhyay, J.[Jayanta],
Larabi, C.[Chaker],
Chaudhury, S.[Santanu],
Lighter and Faster Cross-Concatenated Multi-Scale Residual Block
Based Network for Visual Saliency Prediction,
ICIP21(2503-2507)
IEEE DOI
2201
Training, Measurement, Visualization, Image coding,
Deep architecture, Computer architecture, Predictive models, model parameters
BibRef
Huang, J.[Jing],
Huang, X.L.[Xiao-Lin],
Yang, J.[Jie],
Residual Enhanced Multi-Hypergraph Neural Network,
ICIP21(3657-3661)
IEEE DOI
2201
Convolutional codes, Solid modeling, Correlation, Fuses,
Image processing, Hypergraph learning, multi-hypergraph learning,
3D object classification
BibRef
Srinivas, A.[Aravind],
Lin, T.Y.[Tsung-Yi],
Parmar, N.[Niki],
Shlens, J.[Jonathon],
Abbeel, P.[Pieter],
Vaswani, A.[Ashish],
Bottleneck Transformers for Visual Recognition,
CVPR21(16514-16524)
IEEE DOI
2111
Image segmentation, Adaptation models,
Computational modeling, Botnet, Computer architecture, Object detection
BibRef
Zhang, C.N.[Chao-Ning],
Benz, P.[Philipp],
Argaw, D.M.[Dawit Mureja],
Lee, S.[Seokju],
Kim, J.[Junsik],
Rameau, F.[Francois],
Bazin, J.C.[Jean-Charles],
Kweon, I.S.[In So],
ResNet or DenseNet? Introducing Dense Shortcuts to ResNet,
WACV21(3549-3558)
IEEE DOI
2106
Training, Deep learning, Convolution,
Memory management, Graphics processing units
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],
Shrivastava, A.[Abhinav],
Davis, L.S.[Larry S.],
Knowledge Evolution in Neural Networks,
CVPR21(12838-12847)
IEEE DOI
2111
Knowledge engineering, Training, Measurement, Convolutional codes,
Deep learning, Costs, Neural networks
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
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,
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 .