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Elsevier DOI
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BibRef
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Pekalska, E.[Elÿzbieta],
The dissimilarity space:
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PRL(33), No. 7, 1 May 2012, pp. 826-832.
Elsevier DOI
1203
Award, King Sun Fu.
BibRef
Earlier:
On refining dissimilarity matrices for an improved NN learning,
ICPR08(1-4).
IEEE DOI
0812
Dissimilarity representation; Representation set; Dissimilarity space;
Vector space; Structural pattern recognition
BibRef
Peharz, R.[Robert],
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Pernkopf, F.[Franz],
Domingos, P.[Pedro],
On the Latent Variable Interpretation in Sum-Product Networks,
PAMI(39), No. 10, October 2017, pp. 2030-2044.
IEEE DOI
1709
Bayes methods, Computational modeling, Inference algorithms,
Mixture models, Periodic structures, Probabilistic logic,
Semantics, MPE inference, Sum-product networks,
expectation-maximization, latent variables, mixture, models
See also Sum-product networks: A new deep architecture.
BibRef
Lu, J.[Jie],
Xuan, J.Y.[Jun-Yu],
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Luo, X.F.[Xiang-Feng],
Structural property-aware multilayer network embedding for latent
factor analysis,
PR(76), No. 1, 2018, pp. 228-241.
Elsevier DOI
1801
Multilayer network
BibRef
Cheng, G.,
Li, Z.,
Han, J.,
Yao, X.,
Guo, L.,
Exploring Hierarchical Convolutional Features for Hyperspectral Image
Classification,
GeoRS(56), No. 11, November 2018, pp. 6712-6722.
IEEE DOI
1811
Feature extraction, Measurement, Support vector machines, Training,
Machine learning, Semantics, Hyperspectral imaging,
spectral-spatial feature
BibRef
Guo, A.J.X.,
Zhu, F.,
A CNN-Based Spatial Feature Fusion Algorithm for Hyperspectral
Imagery Classification,
GeoRS(57), No. 9, September 2019, pp. 7170-7181.
IEEE DOI
1909
Feature extraction, Training, Hyperspectral imaging, Testing,
Training data, Adaptation models, Convolutional neural networks,
hyperspectral image classification
BibRef
Liu, Y.[Yang],
Luo, T.J.[Tie-Jian],
The optimization of sum-product network structure learning,
JVCIR(60), 2019, pp. 391-397.
Elsevier DOI
1903
Machine learning, Deep learning, Sum-product network, Structure learning
BibRef
Gong, M.[Maoguo],
Yao, C.Y.[Chuan-Yu],
Xie, Y.[Yu],
Xu, M.L.[Ming-Liang],
Semi-supervised network embedding with text information,
PR(104), 2020, pp. 107347.
Elsevier DOI
2005
Network embedding, Structure preserving, Text representation,
Stacked auto-encoders
BibRef
Iddianozie, C.[Chidubem],
McArdle, G.[Gavin],
Improved Graph Neural Networks for Spatial Networks Using
Structure-Aware Sampling,
IJGI(9), No. 11, 2020, pp. xx-yy.
DOI Link
2012
BibRef
Gui, Y.[Yan],
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Joint learning of visual and spatial features for edit propagation from
a single image,
VC(36), No. 3, March 2020, pp. 469-482.
Springer DOI
2002
BibRef
Tabernik, D.[Domen],
Kristan, M.[Matej],
Leonardis, A.[Aleš],
Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural
Networks,
IJCV(128), No. 8-9, September 2020, pp. 2049-2067.
Springer DOI
2008
BibRef
Earlier:
Spatially-Adaptive Filter Units for Deep Neural Networks,
CVPR18(9388-9396)
IEEE DOI
1812
Standards, Task analysis, Kernel, Neural networks,
Graphics processing units, Strain, Interpolation
BibRef
Dai, Y.P.[Yong-Peng],
Jin, T.[Tian],
Song, Y.K.[Yong-Kun],
Sun, S.L.[Shi-Long],
Wu, C.[Chen],
Convolutional Neural Network with Spatial-Variant Convolution Kernel,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link
2009
BibRef
Ruan, D.S.[Dong-Sheng],
Shi, Y.[Yu],
Wen, J.[Jun],
Zheng, N.G.[Neng-Gan],
Zheng, M.[Min],
Spatially-Aware Context Neural Networks,
IP(30), 2021, pp. 6906-6916.
IEEE DOI
2108
Context modeling, Convolution, Semantics, Computational modeling,
Transforms, Task analysis, Object detection,
context modeling
BibRef
Zhang, M.H.[Ming-Hua],
Luo, H.[Hongling],
Song, W.[Wei],
Mei, H.B.[Hai-Bin],
Su, C.[Cheng],
Spectral-Spatial Offset Graph Convolutional Networks for
Hyperspectral Image Classification,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Wang, D.[Di],
Lan, J.H.[Jin-Hui],
A Deformable Convolutional Neural Network with Spatial-Channel
Attention for Remote Sensing Scene Classification,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link
2112
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
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,
Benchmark testing, Topology, Task analysis,
structural information
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
Zhu, S.[Sihan],
Du, B.[Bo],
Zhang, L.P.[Liang-Pei],
Li, X.[Xue],
Attention-Based Multiscale Residual Adaptation Network for
Cross-Scene Classification,
GeoRS(60), 2022, pp. 1-15.
IEEE DOI
2112
Feature extraction, Task analysis, Data mining, Adaptation models,
Transfer learning, Periodic structures, Manifolds,
residual learning
BibRef
Eberle, O.[Oliver],
Büttner, J.[Jochen],
Kräutli, F.[Florian],
Müller, K.R.[Klaus-Robert],
Valleriani, M.[Matteo],
Montavon, G.[Grégoire],
Building and Interpreting Deep Similarity Models,
PAMI(44), No. 3, March 2022, pp. 1149-1161.
IEEE DOI
2202
Machine learning, Data models, Robustness, Neural networks,
Taylor series, Feature extraction, Deep learning, Similarity,
digital humanities
BibRef
Zhang, J.J.[Jian-Jia],
Zhang, Z.X.[Zhen-Xi],
Wang, L.[Lei],
Zhou, L.P.[Lu-Ping],
Zhang, X.C.[Xiao-Cai],
Liu, M.T.[Meng-Ting],
Wu, W.W.[Wei-Wen],
Kernel-based feature aggregation framework in point cloud networks,
PR(139), 2023, pp. 109439.
Elsevier DOI
2304
Point cloud, Kernel, Feature aggregation, Deep learning, Pooling
BibRef
Li, P.[Peng],
Gao, J.[Jing],
Zhang, J.N.[Jia-Ning],
Jin, S.[Shan],
Chen, Z.K.[Zhi-Kui],
Deep Reinforcement Clustering,
MultMed(25), 2023, pp. 8183-8193.
IEEE DOI
2312
Include structural information.
BibRef
Sun, Z.G.[Zhong-Gui],
Sun, H.[Huichao],
Zhang, M.Z.[Ming-Zhu],
Li, J.[Jie],
Gao, X.B.[Xin-Bo],
A Non-Local Block With Adaptive Regularization Strategy,
SPLetters(31), 2024, pp. 331-335.
IEEE DOI
2402
For deep networks to capture spatial information.
Kernel, Optimization, Convolutional codes, Transforms, Task analysis,
Sun, Convolution, Adaptive regularization,
theoretical interpretation
BibRef
Rezanejad, M.[Morteza],
Wilder, J.[John],
Walther, D.B.[Dirk B.],
Jepson, A.D.[Allan D.],
Dickinson, S.[Sven],
Siddiqi, K.[Kaleem],
Shape-Based Measures Improve Scene Categorization,
PAMI(46), No. 4, April 2024, pp. 2041-2053.
IEEE DOI
2403
DNN biased on color and texture, not structure.
Visualization, Organizations, Convolutional neural networks,
Transforms, Feature extraction, Task analysis, Observers, shape based measures
BibRef
Lin, G.F.[Guang-Feng],
Wei, W.C.[Wen-Chao],
Kang, X.B.[Xia-Bing],
Liao, K.Y.[Kai-Yang],
Zhang, E.[Erhu],
Deep graph layer information mining convolutional network,
PR(154), 2024, pp. 110593.
Elsevier DOI
2406
Deep learning, Graph convolutional neural network,
Graph learning, Hierarchical structure
BibRef
Cao, Y.K.[Yu-Kun],
Feng, Y.[Yuan],
Wang, H.[Hairu],
Xie, X.[Xike],
Zhou, S.K.[S. Kevin],
Learning to Sketch:
A Neural Approach to Item Frequency Estimation in Streaming Data,
PAMI(46), No. 11, November 2024, pp. 7136-7153.
IEEE DOI
2410
Streams, Vectors, Data structures, Artificial neural networks,
Frequency estimation, Task analysis, Streaming media,
memory-augmented neural networks
BibRef
Shen, J.[Jing],
Huo, C.L.[Chun-Lei],
Xiang, S.M.[Shi-Ming],
Siamese InternImage for Change Detection,
RS(16), No. 19, 2024, pp. 3642.
DOI Link
2410
Deformable convolution. Capture spatial changes.
BibRef
Veksler, O.[Olga],
Boykov, Y.Y.[Yuri Y.],
Sparse Non-Local CRF With Applications,
PAMI(47), No. 2, February 2025, pp. 773-788.
IEEE DOI
2501
Conditional Random Fields.
Tail, Deep learning, Image color analysis, Optimization, Labeling,
Computational modeling, Color, Spatial coherence,
weak supervision
BibRef
Wu, Z.Y.[Zhen-Yu],
Wang, W.[Wei],
Wang, L.[Lin],
Li, Y.[Yacong],
Lv, F.[Fengmao],
Xia, Q.[Qing],
Chen, C.L.Z.[Cheng-Li-Zhao],
Hao, A.[Aimin],
Li, S.[Shuo],
Pixel is All You Need: Adversarial Spatio-Temporal Ensemble Active
Learning for Salient Object Detection,
PAMI(47), No. 2, February 2025, pp. 858-877.
IEEE DOI
2501
Training, Computational modeling, Object detection, Annotations,
Uncertainty, Computational efficiency, Labeling, Estimation, Costs,
salient object detection
BibRef
Peng, Y.P.[Yao-Peng],
Wang, H.X.[Hong-Xiao],
Sonka, M.[Milan],
Chen, D.Z.[Danny Z.],
PHG-Net: Persistent Homology Guided Medical Image Classification*,
WACV24(7568-7577)
IEEE DOI
2404
Structure is important, not just pixel values.
Costs, Network topology, Feature extraction, Transformers, Vectors,
Topology, Convolutional neural networks, Applications,
Biomedical / healthcare / medicine
BibRef
Djenouri, Y.[Youcef],
Belbachir, A.N.[Ahmed Nabil],
Michalak, T.[Tomasz],
Yazidi, A.[Anis],
Shapley Deep Learning: A Consensus for General-Purpose Vision Systems,
REDLCV23(1216-1225)
IEEE DOI
2401
BibRef
Grimaldi, M.[Matteo],
Ganji, D.C.[Darshan C.],
Lazarevich, I.[Ivan],
Deeplite, S.S.[Sudhakar Sah],
Accelerating Deep Neural Networks via Semi-Structured Activation
Sparsity,
REDLCV23(1171-1180)
IEEE DOI Code:
WWW Link.
2401
BibRef
Cao, G.P.[Gui-Ping],
Luo, S.D.[Sheng-Da],
Huang, W.J.[Wen-Jian],
Lan, X.Y.[Xiang-Yuan],
Jiang, D.M.[Dong-Mei],
Wang, Y.W.[Yao-Wei],
Zhang, J.G.[Jian-Guo],
Strip-MLP: Efficient Token Interaction for Vision MLP,
ICCV23(1494-1504)
IEEE DOI Code:
WWW Link.
2401
Multilayer perceptron.
BibRef
Singh, R.[Rajhans],
Shukla, A.[Ankita],
Turaga, P.[Pavan],
Improving Shape Awareness and Interpretability in Deep Networks Using
Geometric Moments,
DLGC23(4159-4168)
IEEE DOI
2309
BibRef
Chen, Y.[Ya],
Zheng, L.[Liang],
Zheng, J.[Jin],
Cheng, J.J.[Jun-Jie],
Remote Sensing Image Scene Classification Method Based on Semantic
and Spatial Interactive Information,
ICIVC22(436-441)
IEEE DOI
2301
Visualization, Image analysis, Convolution, Computational modeling, Semantics,
Neural networks, Transformers, remote sensing, attentional mechanism
BibRef
Ho, T.K.[Tin Kam],
Complexity of Representations in Deep Learning,
ICPR22(2657-2663)
IEEE DOI
2212
Training, Deep learning, Neural networks, Training data, Robustness,
Complexity theory, Behavioral sciences, representation learning,
data complexity
BibRef
Berardi, G.[Gianluca],
de Luigi, L.[Luca],
Salti, S.[Samuele],
di Stefano, L.[Luigi],
Learning the Space of Deep Models,
ICPR22(2482-2488)
IEEE DOI
2212
Representation learning, Deep learning, Training, Interpolation,
Shape, Data models
BibRef
Chen, R.H.[Rong-Han],
Cong, Y.[Yang],
The Devil is in the Pose: Ambiguity-free 3D Rotation-invariant
Learning via Pose-aware Convolution,
CVPR22(7462-7471)
IEEE DOI
2210
Adapt kernels to pose.
Deep learning, Convolution, Shape,
Computational efficiency, Recognition: detection, categorization,
grouping and shape analysis
BibRef
Singhal, U.[Utkarsh],
Xing, Y.F.[Yi-Fei],
Yu, S.X.[Stella X.],
Co-domain Symmetry for Complex-Valued Deep Learning,
CVPR22(671-680)
IEEE DOI
2210
Manifolds, Deep learning, Image color analysis,
Computational modeling, Complex networks, retrieval
BibRef
Edstedt, J.[Johan],
Athanasiadis, I.[Ioannis],
Wadenbäck, M.[Mårten],
Felsberg, M.[Michael],
DKM: Dense Kernelized Feature Matching for Geometry Estimation,
CVPR23(17765-17775)
IEEE DOI
2309
BibRef
Melnyk, P.[Pavlo],
Felsberg, M.[Michael],
Wadenbäck, M.[Mårten],
Embed Me If You Can: A Geometric Perceptron,
ICCV21(1256-1264)
IEEE DOI
2203
Geometry, Solid modeling, Shape, Computational modeling, Neurons,
Nonhomogeneous media, Recognition and classification,
Machine learning architectures and formulations
BibRef
Li, Y.[Yang],
Kan, S.C.[Shi-Chao],
Yuan, J.[Jianhe],
Cao, W.M.[Wen-Ming],
He, Z.H.[Zhi-Hai],
Spatial Assembly Networks for Image Representation Learning,
CVPR21(13871-13880)
IEEE DOI
2111
Measurement, Deep learning, Training, Visualization, Layout,
Image retrieval, Transforms
BibRef
Finnveden, L.[Lukas],
Jansson, Y.[Ylva],
Lindeberg, T.[Tony],
Understanding when spatial transformer networks do not support
invariance, and what to do about it,
ICPR21(3427-3434)
IEEE DOI
2105
a way to do translation invariance in CNN.
Location awareness, Transforms,
Complexity theory, Convolutional neural networks
BibRef
Ge, Y.H.[Yun-Hao],
Xiao, Y.[Yao],
Xu, Z.[Zhi],
Zheng, M.[Meng],
Karanam, S.[Srikrishna],
Chen, T.[Terrence],
Itti, L.[Laurent],
Wu, Z.Y.[Zi-Yan],
A Peek Into the Reasoning of Neural Networks:
Interpreting with Structural Visual Concepts,
CVPR21(2195-2204)
IEEE DOI
2111
Deep learning, Bridges, Visualization, Correlation,
Decision making, Artificial neural networks
BibRef
Li, Y.[Yang],
Tanaka, Y.[Yuichi],
Structural Features in Feature Space for Structure-Aware Graph
Convolution,
ICIP21(3158-3162)
IEEE DOI
2201
Convolution, Image processing, Signal processing algorithms,
Graph neural networks, Classification algorithms,
deep learning
BibRef
Patil, A.G.[Akshay Gadi],
Li, M.[Manyi],
Fisher, M.[Matthew],
Savva, M.[Manolis],
Zhang, H.[Hao],
LayoutGMN: Neural Graph Matching for Structural Layout Similarity,
CVPR21(11043-11052)
IEEE DOI
2111
Measurement, Deep learning, Convolution,
Computational modeling, Layout, Graph neural networks
BibRef
Carbonell, M.[Manuel],
Riba, P.[Pau],
Villegas, M.[Mauricio],
Fornés, A.[Alicia],
Lladós, J.[Josep],
Named Entity Recognition and Relation Extraction with Graph Neural
Networks in Semi Structured Documents,
ICPR21(9622-9627)
IEEE DOI
2105
Information retrieval, Graph neural networks, Data mining,
Task analysis, Tuning, Testing, Strain,
Graph Neural Networks
BibRef
Fan, Y.[Yue],
Xian, Y.Q.[Yong-Qin],
Losch, M.M.[Max Maria],
Schiele, B.[Bernt],
Analyzing the Dependency of ConvNets on Spatial Information,
GCPR20(101-115).
Springer DOI
2110
BibRef
Schneider, J.[Johannes],
Locality-Promoting Representation Learning,
ICPR21(8061-8068)
IEEE DOI
2105
Spatial filters, Convolutional neural networks
BibRef
Chelali, M.[Mohamed],
Kurtz, C.[Camille],
Puissant, A.[Anne],
Vincent, N.[Nicole],
Classification of spatially enriched pixel time series with
convolutional neural networks,
ICPR21(5310-5317)
IEEE DOI
2105
Visualization, Image segmentation, Image color analysis,
Time series analysis, Soil, Feature extraction, Spatiotemporal phenomena
BibRef
Chi, L.[Lu],
Yuan, Z.H.[Ze-Huan],
Mu, Y.D.[Ya-Dong],
Wang, C.H.[Chang-Hu],
Non-Local Neural Networks With Grouped Bilinear Attentional
Transforms,
CVPR20(11801-11810)
IEEE DOI
2008
Model spatial.
Convolution, Transforms, Kernel,
Task analysis, Biological neural networks
BibRef
Mukundan, A.[Arun],
Tolias, G.[Giorgos],
Chum, O.[Ondrej],
Explicit Spatial Encoding for Deep Local Descriptors,
CVPR19(9386-9395).
IEEE DOI
2002
BibRef
Shevlev, I.[Irina],
Avidan, S.[Shai],
Co-Occurrence Neural Network,
CVPR19(4792-4799).
IEEE DOI
2002
Adding spatial information.
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
Shah, S.A.A.[Syed Afaq Ali],
Spatial Hierarchical Analysis Deep Neural Network for RGB-D Object
Recognition,
PSIVT19(183-193).
Springer DOI
2003
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, Y.[Yan],
Xie, L.X.[Ling-Xi],
Qiao, S.Y.[Si-Yuan],
Zhang, Y.[Ya],
Zhang, W.J.[Wen-Jun],
Yuille, A.L.[Alan L.],
Multi-scale Spatially-Asymmetric Recalibration for Image Classification,
ECCV18(XIII: 523-539).
Springer DOI
1810
To get spatial information in features using multiple scales.
BibRef
Zhao, H.S.[Heng-Shuang],
Zhang, Y.[Yi],
Liu, S.[Shu],
Shi, J.P.[Jian-Ping],
Loy, C.C.[Chen Change],
Lin, D.[Dahua],
Jia, J.Y.[Jia-Ya],
PSANet: Point-wise Spatial Attention Network for Scene Parsing,
ECCV18(IX: 270-286).
Springer DOI
1810
BibRef
Zhang, J.Y.[Jing-Yang],
Jia, K.G.[Kai-Ge],
Yang, P.S.[Peng-Shuai],
Qiao, F.[Fei],
Wei, Q.[Qi],
Liu, X.J.[Xin-Jun],
Yang, H.Z.[Hua-Zhong],
MINTIN: Maxout-Based and Input-Normalized Transformation Invariant
Neural Network,
ICIP18(3014-3018)
IEEE DOI
1809
Need to deal with spatial variance in input.
Feature extraction, Neural networks, Network topology,
Error analysis, Kernel, Calibration, Maxout
BibRef
Zhang, Y.,
Guo, Y.,
Jin, Y.,
Luo, Y.,
He, Z.,
Lee, H.,
Unsupervised Discovery of Object Landmarks as Structural
Representations,
CVPR18(2694-2703)
IEEE DOI
1812
Visualization, Neural networks, Decoding, Image reconstruction,
Training, Detectors
BibRef
Xu, J.W.[Jing-Wei],
Ni, B.B.[Bing-Bing],
Li, Z.F.[Ze-Fan],
Cheng, S.[Shuo],
Yang, X.K.[Xiao-Kang],
Structure Preserving Video Prediction,
CVPR18(1460-1469)
IEEE DOI
1812
RNN structure.
Kernel, Task analysis,
Dynamics, Decoding, Predictive models
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
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
Zhu, Y.,
Zhao, C.,
Wang, J.,
Zhao, X.,
Wu, Y.,
Lu, H.,
CoupleNet:
Coupling Global Structure with Local Parts for Object Detection,
ICCV17(4146-4154)
IEEE DOI
1802
convolution, image classification, neural nets, object detection,
Convolutional Neural Network detectors, CoupleNet,
Visualization
BibRef
Ji, J.,
Mei, S.,
Liu, X.,
Li, X.,
Zeng, S.,
Wang, Z.,
Exploring Kernel Based Spatial Context for CNN Based Hyperspectral
Image Classification,
DICTA17(1-7)
IEEE DOI
1804
Gaussian processes, geophysical image processing,
hyperspectral imaging, image classification,
Training
BibRef
Hu, H.X.[He-Xiang],
Zhou, G.T.[Guang-Tong],
Deng, Z.W.[Zhi-Wei],
Liao, Z.C.[Zi-Cheng],
Mori, G.[Greg],
Learning Structured Inference Neural Networks with Label Relations,
CVPR16(2960-2968)
IEEE DOI
1612
Network for each layer of representation.
BibRef
Kuzmenko, A.,
Zagoruyko, N.,
Structure relaxation method for self-organizing neural networks,
ICPR04(IV: 589-592).
IEEE DOI
0409
BibRef
Kröner, S.[Sabine],
A structured neural network invariant to cyclic shifts and rotations,
CAIP97(384-391).
Springer DOI
9709
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Loss Functions, Triplet Loss Function, Deep Learning, Neural Netowrks .