14.5.7.3.2 Residual Neural Networks, ResNet

Chapter Contents (Back)
Feature Description. Residual Neural Networks. A subset.

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

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

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

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


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.[Dongyoon], 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], Gigučre, 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, Three-dimensional, displays BibRef

Trottier, L.[Ludovic], Gigučre, 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 .


Last update:Jan 25, 2019 at 17:55:53