14.5.9.8.14 Deep Learning, Deep Nets, DNN

Bengio, Y.,
Learning deep architectures for AI,
FTML(1), No. 1, 2009, pp. 1-127.
DOI Link 1609
BibRef

Zhao, W.Z.[Wen-Zhi], Du, S.H.[Shi-Hong],
Learning multiscale and deep representations for classifying remotely sensed imagery,
PandRS(113), No. 1, 2016, pp. 155-165.
Elsevier DOI 1602
Multiscale convolutional neural network (MCNN) BibRef

Barat, C.[Cécile], Ducottet, C.[Christophe],
String representations and distances in deep Convolutional Neural Networks for image classification,
PR(54), No. 1, 2016, pp. 104-115.
Elsevier DOI 1603
Convolutional Neural Network BibRef

Greenspan, H., van Ginneken, B., Summers, R.M.,
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique,
MedImg(35), No. 5, May 2016, pp. 1153-1159.
IEEE DOI 1605
Artificial neural networks BibRef

Murthy, V.N., Singh, V., Chen, T., Manmatha, R., Comaniciu, D.,
Deep Decision Network for Multi-class Image Classification,
CVPR16(2240-2248)
IEEE DOI 1612
BibRef

Mansoor, A., Cerrolaza, J.J., Idrees, R., Biggs, E., Alsharid, M.A., Avery, R.A., Linguraru, M.G.,
Deep Learning Guided Partitioned Shape Model for Anterior Visual Pathway Segmentation,
MedImg(35), No. 8, August 2016, pp. 1856-1865.
IEEE DOI 1608
Biomedical optical imaging BibRef

van Noord, N.[Nanne], Postma, E.[Eric],
Learning scale-variant and scale-invariant features for deep image classification,
PR(61), No. 1, 2017, pp. 583-592.
Elsevier DOI 1609
Convolutional Neural Networks BibRef

Du, J.[Jun], Xu, Y.[Yong],
Hierarchical deep neural network for multivariate regression,
PR(63), No. 1, 2017, pp. 149-157.
Elsevier DOI 1612
Divide and Conquer BibRef

Guo, S., Huang, W., Wang, L., Qiao, Y.,
Locally Supervised Deep Hybrid Model for Scene Recognition,
IP(26), No. 2, February 2017, pp. 808-820.
IEEE DOI 1702
data compression BibRef

Masoumi, M.[Majid], Ben Hamza, A.,
Spectral shape classification: A deep learning approach,
JVCIR(43), No. 1, 2017, pp. 198-211.
Elsevier DOI 1702
Deep learning BibRef

Luciano, L.[Lorenzo], Ben Hamza, A.,
Deep learning with geodesic moments for 3D shape classification,
PRL(105), 2018, pp. 182-190.
Elsevier DOI 1804
Geodesic moments, Deep learning, Laplace-Beltrami, Stacked autoencoders, Shape classification BibRef

Luciano, L.[Lorenzo], Ben Hamza, A.,
Deep similarity network fusion for 3D shape classification,
VC(35), No. 6-8, June 2018, pp. 1171-1180.
WWW Link. 1906
BibRef

Li, Y.[Yao], Liu, L.Q.[Ling-Qiao], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Mining Mid-level Visual Patterns with Deep CNN Activations,
IJCV(121), No. 3, February 2017, pp. 344-364.
Springer DOI 1702
BibRef

Sun, W.C.[Wei-Chen], Su, F.[Fei],
A novel companion objective function for regularization of deep convolutional neural networks,
IVC(60), No. 1, 2017, pp. 58-63.
Elsevier DOI 1704
BibRef
Earlier:
Regularization of deep neural networks using a novel companion objective function,
ICIP15(2865-2869)
IEEE DOI 1512
Convolutional neural network. Companion objective function BibRef

Sun, W.C.[Wei-Chen], Su, F.[Fei], Wang, L.Q.[Lei-Quan],
Improving deep neural networks with multilayer maxout networks,
VCIP14(334-337)
IEEE DOI 1504
filtering theory BibRef

Shi, C.[Cheng], Pun, C.M.[Chi-Man],
Superpixel-based 3D deep neural networks for hyperspectral image classification,
PR(74), No. 1, 2018, pp. 600-616.
Elsevier DOI 1711
Hyperspectral image classification BibRef

Shi, C.[Cheng], Pun, C.M.[Chi-Man],
Multiscale Superpixel-Based Hyperspectral Image Classification Using Recurrent Neural Networks With Stacked Autoencoders,
MultMed(22), No. 2, February 2020, pp. 487-501.
IEEE DOI 2001
Recurrent neural networks, Correlation, Feature extraction, Neurons, Hyperspectral imaging, Principal component analysis, Stacked autoencoders BibRef

Zagoruyko, S.[Sergey], Komodakis, N.[Nikos],
Deep compare: A study on using convolutional neural networks to compare image patches,
CVIU(164), No. 1, 2017, pp. 38-55.
Elsevier DOI 1801
BibRef
Earlier:
Learning to compare image patches via convolutional neural networks,
CVPR15(4353-4361)
IEEE DOI 1510
Descriptor learning BibRef

Simonovsky, M.[Martin], Komodakis, N.[Nikos],
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs,
CVPR17(29-38)
IEEE DOI 1711
Convolution, Machine learning, Neural networks, Spectral analysis, Standards, BibRef

Simonovsky, M.[Martin], Komodakis, N.[Nikos],
OnionNet: Sharing Features in Cascaded Deep Classifiers,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Liu, N.[Na], Lu, X.K.[Xian-Kai], Wan, L.H.[Li-Hong], Huo, H.[Hong], Fang, T.[Tao],
Improving the Separability of Deep Features with Discriminative Convolution Filters for RSI Classification,
IJGI(7), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Liu, Y.[Yu], Liu, L.[Li], Guo, Y.M.[Yan-Ming], Lew, M.S.[Michael S.],
Learning visual and textual representations for multimodal matching and classification,
PR(84), 2018, pp. 51-67.
Elsevier DOI 1809
Vision and language, Multimodal matching, Multimodal classification, Deep learning BibRef

Ben Hamida, A., Benoit, A., Lambert, P., Ben Amar, C.,
3-D Deep Learning Approach for Remote Sensing Image Classification,
GeoRS(56), No. 8, August 2018, pp. 4420-4434.
IEEE DOI 1808
geophysical image processing, hyperspectral imaging, image classification, learning (artificial intelligence), remote sensing (RS) BibRef

Piramanayagam, S.[Sankaranarayanan], Saber, E.[Eli], Schwartzkopf, W.[Wade], Koehler, F.W.[Frederick W.],
Supervised Classification of Multisensor Remotely Sensed Images Using a Deep Learning Framework,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Mountrakis, G.[Giorgos], Li, J.[Jun], Lu, X.Q.[Xiao-Qiang], Hellwich, O.[Olaf],
Deep learning for remotely sensed data,
PandRS(145), 2018, pp. 1-2.
Elsevier DOI 1810
BibRef

Xu, J., Wang, C., Qi, C., Shi, C., Xiao, B.,
Unsupervised Semantic-Based Aggregation of Deep Convolutional Features,
IP(28), No. 2, February 2019, pp. 601-611.
IEEE DOI 1811
feedforward neural nets, image classification, image representation, image retrieval, unsupervised learning, semantic detectors BibRef

Chevalier, M.[Marion], Thome, N.[Nicolas], Hénaff, G.[Gilles], Cord, M.[Matthieu],
Classifying low-resolution images by integrating privileged information in deep CNNs,
PRL(116), 2018, pp. 29-35.
Elsevier DOI 1812
Image classification, Deep convolutional neural networks, Learning using privileged information. BibRef

Zhang, Z.X.[Zhao-Xiang], Shan, S.G.[Shi-Guang], Fang, Y.[Yi], Shao, L.[Ling],
Deep Learning for Pattern Recognition,
PRL(119), 2019, pp. 1-2.
Elsevier DOI 1902
BibRef

Wang, J.[Jia], Liu, C.[Chen], Fu, T.[Tian], Zheng, L.[Lili],
Research on automatic target detection and recognition based on deep learning,
JVCIR(60), 2019, pp. 44-50.
Elsevier DOI 1903
Image processing, Target detection, Target recognition, In-depth learning BibRef

Zhang, J.[Ji], Mei, K.[Kuizhi], Zheng, Y.[Yu], Fan, J.P.[Jian-Ping],
Learning multi-layer coarse-to-fine representations for large-scale image classification,
PR(91), 2019, pp. 175-189.
Elsevier DOI 1904
Visual-semantic tree, Inter-category correlation, Multi-task learning, Deep convolutional neural network, Large-scale image classification BibRef

Liu, D., Cheng, B., Wang, Z., Zhang, H., Huang, T.S.,
Enhance Visual Recognition Under Adverse Conditions via Deep Networks,
IP(28), No. 9, Sep. 2019, pp. 4401-4412.
IEEE DOI 1908
image recognition, image restoration, learning (artificial intelligence), neural nets, image recognition BibRef

Cai, Z.Y.[Zi-Yun], Long, Y.[Yang], Shao, L.[Ling],
Classification complexity assessment for hyper-parameter optimization,
PRL(125), 2019, pp. 396-403.
Elsevier DOI 1909
Hyper-parameter optimization, Deep learning, Classification complexity measure BibRef

Li, Y.Y.[Yu-Yuan], Zhang, D.[Dong], Lee, D.J.[Dah-Jye],
IIRNet: A lightweight deep neural network using intensely inverted residuals for image recognition,
IVC(92), 2019, pp. 103819.
Elsevier DOI 1912
Convolutional neural network (CNN), Lightweight CNN, Image recognition, Low-redundancy, Model size, Computation complexity BibRef

He, C.[Chu], Zhang, Q.Y.[Qing-Yi], Qu, T.[Tao], Wang, D.W.[Ding-Wen], Liao, M.S.[Ming-Sheng],
Remote Sensing and Texture Image Classification Network Based on Deep Learning Integrated with Binary Coding and Sinkhorn Distance,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Tsiligianni, E., Deligiannis, N.,
Deep Coupled-Representation Learning for Sparse Linear Inverse Problems With Side Information,
SPLetters(26), No. 12, December 2019, pp. 1768-1772.
IEEE DOI 2001
computational complexity, image reconstruction, image representation, inverse problems, designing deep neural networks BibRef

Guo, J.[Jun], Yuan, X.[Xuan], Xu, P.F.[Peng-Fei], Bai, H.[Hao], Liu, B.Y.[Bao-Ying],
Improved image clustering with deep semantic embedding,
PRL(130), 2020, pp. 225-233.
Elsevier DOI 2002
Semantic embedding, Image clustering, Deep neural networks, Deep autoencoder BibRef

Kim, E.Y.[Eu Young], Shin, S.Y.[Seung Yeon], Lee, S.[Soochahn], Lee, K.J.[Kyong Joon], Lee, K.H.[Kyoung Ho], Lee, K.M.[Kyoung Mu],
Triplanar convolution with shared 2D kernels for 3D classification and shape retrieval,
CVIU(193), 2020, pp. 102901.
Elsevier DOI 2003
3D vision, Medical image, Deep learning BibRef

Yavartanoo, M.[Mohsen], Hung, S.H.[Shih-Hsuan], Neshatavar, R.[Reyhaneh], Zhang, Y.[Yue], Lee, K.M.[Kyoung Mu],
PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation,
3DV21(1014-1023)
IEEE DOI 2201
Convolutional codes, Geometry, Deep learning, Image segmentation, Shape, Aggregates BibRef

Yavartanoo, M.[Mohsen], Kim, E.Y.[Eu Young], Lee, K.M.[Kyoung Mu],
SPNet: Deep 3D Object Classification and Retrieval Using Stereographic Projection,
ACCV18(V:691-706).
Springer DOI 1906
BibRef

Do, T.T.[Thanh-Toan], Hoang, T.[Tuan], Tan, D.K.L.[Dang-Khoa Le], Doan, A.D.[Anh-Dzung], Cheung, N.M.[Ngai-Man],
Compact Hash Code Learning With Binary Deep Neural Network,
MultMed(22), No. 4, April 2020, pp. 992-1004.
IEEE DOI 2004
Binary constraint optimization, image search, learning to hash BibRef

Do, T.T.[Thanh-Toan], Hoang, T.[Tuan], Tan, D.K.L.[Dang-Khoa Le], Pham, T.[Trung], Le, H.[Huu], Cheung, N.M.[Ngai-Man], Reid, I.D.[Ian D.],
Binary Constrained Deep Hashing Network for Image Retrieval Without Manual Annotation,
WACV19(695-704)
IEEE DOI 1904
binary codes, image representation, image retrieval, neural nets, compact representation methods, image retrieval, Feature extraction BibRef

Do, T.T.[Thanh-Toan], Doan, A.D.[Anh-Dzung], Cheung, N.M.[Ngai-Man],
Learning to Hash with Binary Deep Neural Network,
ECCV16(V: 219-234).
Springer DOI 1611
BibRef

Tan, D.K.L.[Dang-Khoa Le], Do, T.T.[Thanh-Toan], Cheung, N.M.[Ngai-Man],
Supervised Hashing with End-to-End Binary Deep Neural Network,
ICIP18(3019-3023)
IEEE DOI 1809
Feature extraction, Training, Binary codes, Testing, Visualization, Image retrieval, Optimization, deep neural network, BibRef

Ruthotto, L.[Lars], Haber, E.[Eldad],
Deep Neural Networks Motivated by Partial Differential Equations,
JMIV(62), No. 3, April 2020, pp. 352-364.
Springer DOI 2004
BibRef

Forcén, J.I.[Juan Ignacio], Pagola, M.[Miguel], Barrenechea, E.[Edurne], Bustince, H.[Humberto],
Co-occurrence of deep convolutional features for image search,
IVC(97), 2020, pp. 103909.
Elsevier DOI 2005
BibRef
Earlier:
Aggregation of Deep Features for Image Retrieval Based on Object Detection,
IbPRIA19(I:553-564).
Springer DOI 1910
Image retrieval, Feature aggregation, Pooling BibRef

Passalis, N.[Nikolaos], Raitoharju, J.[Jenni], Tefas, A.[Anastasios], Gabbouj, M.[Moncef],
Efficient adaptive inference for deep convolutional neural networks using hierarchical early exits,
PR(105), 2020, pp. 107346.
Elsevier DOI 2006
Adaptive inference, Early exits, Bag-of-Features, Deep convolutional neural networks, Hierarchical representations BibRef

Huang, L.[Lei], Liu, X.L.[Xiang-Long], Qin, J.[Jie], Zhu, F.[Fan], Liu, L.[Li], Shao, L.[Ling],
Projection based weight normalization: Efficient method for optimization on oblique manifold in DNNs,
PR(105), 2020, pp. 107317.
Elsevier DOI 2006
Deep learning, Weight normalization, Oblique manifold, Image classification BibRef

Cococcioni, M.[Marco], Rossi, F.[Federico], Ruffaldi, E.[Emanuele], Saponara, S.[Sergio],
Fast deep neural networks for image processing using posits and ARM scalable vector extension,
RealTimeIP(17), No. 3, June 2020, pp. 759-771.
Springer DOI 2006
BibRef

Tanaka, M.[Masayuki],
Weighted sigmoid gate unit for an activation function of deep neural network,
PRL(135), 2020, pp. 354-359.
Elsevier DOI 2006
Deep neural network, Activation function, Relu BibRef

Ulyanov, D.[Dmitry], Vedaldi, A.[Andrea], Lempitsky, V.[Victor],
Deep Image Prior,
IJCV(128), No. 7, July 2020, pp. 1867-1888.
Springer DOI 2007
show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. BibRef

Shin, D., Yoo, H.,
The Heterogeneous Deep Neural Network Processor With a Non-von Neumann Architecture,
PIEEE(108), No. 8, August 2020, pp. 1245-1260.
IEEE DOI 2007
Hardware, Biological neural networks, Computers, Recurrent neural networks, Deep learning, recurrent neural networks (RNNs) BibRef

Zhu, R., Dornaika, F., Ruichek, Y.,
Semi-supervised elastic manifold embedding with deep learning architecture,
PR(107), 2020, pp. 107425.
Elsevier DOI 2008
Graph-based embedding, Elastic embedding, Deep learning architecture, Supervised learning, Semi-supervised learning BibRef

Chen, Z.[Zhi], Ho, P.H.[Pin-Han],
A generic shift-norm-activation approach for deep learning,
PR(109), 2021, pp. 107609.
Elsevier DOI 2009
Activation, Normalization, CNN, Shifting, Deep learning BibRef

Chen, G.[Gang], Srihari, S.N.[Sargur N.],
Revisiting hierarchy: Deep learning with orthogonally constrained prior for classification,
PRL(140), 2020, pp. 214-221.
Elsevier DOI 2012
Hierarchical prior, Classification, Deep learning BibRef

Fukushima, K.,
Artificial Vision by Deep CNN Neocognitron,
SMCS(51), No. 1, January 2021, pp. 76-90.
IEEE DOI 2101
Visualization, Focusing, Visual systems, Biology, Convolutional neural networks, History, selective attention BibRef

Qi, Z.A.[Zhong-Ang], Khorram, S.[Saeed], Fuxin, L.[Li],
Embedding deep networks into visual explanations,
AI(292), 2021, pp. 103435.
Elsevier DOI 2102
Deep neural networks, Embedding, Visual explanations BibRef

Messina, N.[Nicola], Amato, G.[Giuseppe], Carrara, F.[Fabio], Gennaro, C.[Claudio], Falchi, F.[Fabrizio],
Solving the same-different task with convolutional neural networks,
PRL(143), 2021, pp. 75-80.
Elsevier DOI 2102
AI, Deep learning, Abstract reasoning, Relational reasoning, Convolutional neural, Networks BibRef

Amato, G.[Giuseppe], Carrara, F.[Fabio], Falchi, F.[Fabrizio], Gennaro, C.[Claudio], Lagani, G.[Gabriele],
Hebbian Learning Meets Deep Convolutional Neural Networks,
CIAP19(I:324-334).
Springer DOI 1909
BibRef

Yuille, A.L.[Alan L.], Liu, C.X.[Chen-Xi],
Deep Nets: What have They Ever Done for Vision?,
IJCV(129), No. 3, March 2021, pp. 781-802.
Springer DOI 2103
We argue that Deep Nets in their current form are unlikely to be able to overcome the fundamental problem of computer vision, namely how to deal with the combinatorial explosion, caused by the enormous complexity of natural images, and obtain the rich understanding of visual scenes that the human visual achieves. BibRef

Balestriero, R.[Randall], Baraniuk, R.G.[Richard G.],
Mad Max: Affine Spline Insights Into Deep Learning,
PIEEE(109), No. 5, May 2021, pp. 704-727.
IEEE DOI 2105
Splines (mathematics), Standards, Deep learning, Convolution, Task analysis, Recurrent neural networks, Quantization (signal), Voronoi diagram BibRef

Zeng, X.F.[Xian-Fang], Wu, W.X.[Wen-Xuan], Tian, G.Z.[Guang-Zhong], Li, F.X.[Fu-Xin], Liu, Y.[Yong],
Deep Superpixel Convolutional Network for Image Recognition,
SPLetters(28), 2021, pp. 922-926.
IEEE DOI 2106
Convolution, Task analysis, Standards, Image recognition, Kernel, Feature extraction, Deep learning, superpixel BibRef

Zhao, B.X.[Bao-Xin], Xiong, H.Y.[Hao-Yi], Bian, J.[Jiang], Guo, Z.S.[Zhi-Shan], Xu, C.Z.[Cheng-Zhong], Dou, D.[Dejing],
COMO: Efficient Deep Neural Networks Expansion With COnvolutional MaxOut,
MultMed(23), 2021, pp. 1722-1730.
IEEE DOI 2106
Convolution, Spatial resolution, Convolutional neural networks, Deep learning, Transforms, neural networks BibRef

Wang, X.[Xin], Wang, S.Y.[Shi-Yi], Ning, C.[Chen], Zhou, H.Y.[Hui-Yu],
Enhanced Feature Pyramid Network With Deep Semantic Embedding for Remote Sensing Scene Classification,
GeoRS(59), No. 9, September 2021, pp. 7918-7932.
IEEE DOI 2109
Feature extraction, Semantics, Spatial resolution, Convolution, Remote sensing, Deconvolution, Task analysis, scene classification BibRef

Yang, S.J.[Shi-Jie], Li, L.[Liang], Wang, S.H.[Shu-Hui], Zhang, W.G.[Wei-Gang], Huang, Q.M.[Qing-Ming], Tian, Q.[Qi],
Graph Regularized Encoder-Decoder Networks for Image Representation Learning,
MultMed(23), 2021, pp. 3124-3136.
IEEE DOI 2109
BibRef
Earlier: A1, A2, A3, A4, A5, Only:
A Graph Regularized Deep Neural Network for Unsupervised Image Representation Learning,
CVPR17(7053-7061)
IEEE DOI 1711
Laplace equations, Visualization, Manifolds, Image reconstruction, Task analysis, Decoding, Semantics, Auto-encoder, encoder-decoder, image representation learning. Neural networks, Robustness. BibRef

Chen, Z.D.[Zhao-Dong], Deng, L.[Lei], Wang, B.Y.[Bang-Yan], Li, G.Q.[Guo-Qi], Xie, Y.[Yuan],
A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks,
PAMI(44), No. 1, January 2022, pp. 13-31.
IEEE DOI 2112
Jacobian matrices, Explosions, Measurement, Biological neural networks, Probability, Libraries, gradient norm equality BibRef

Sommer, S.[Stefan], Bronstein, A.M.[Alex M.],
Horizontal Flows and Manifold Stochastics in Geometric Deep Learning,
PAMI(44), No. 2, February 2022, pp. 811-822.
IEEE DOI 2201
Manifolds, Convolution, Machine learning, Geometry, Stochastic processes, Bridges, Neural networks, bridge sampling BibRef

Yan, M.[Ming], Yang, J.X.[Jian-Xi], Chen, C.[Cen], Zhou, J.T.Y.[Joey Tian-Yi], Pan, Y.[Yi], Zeng, Z.[Zeng],
Enhanced gradient learning for deep neural networks,
IET-IPR(16), No. 2, 2022, pp. 365-377.
DOI Link 2201
BibRef

Li, Z.Y.[Zheng-Ying], Huang, H.[Hong], Zhang, Z.[Zhen], Shi, G.Y.[Guang-Yao],
Manifold-Based Multi-Deep Belief Network for Feature Extraction of Hyperspectral Image,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Nakamura, K.[Kensuke], Soatto, S.[Stefano], Hong, B.W.[Byung-Woo],
Stochastic batch size for adaptive regularization in deep network optimization,
PR(129), 2022, pp. 108776.
Elsevier DOI 2206
Deep network optimization, Adaptive regularization, Stochastic gradient descent, Adaptive mini-batch size BibRef

Li, G.Q.[Guo-Qiang], Fang, Q.[Qi], Zha, L.L.[Lin-Lin], Gao, X.[Xin], Zheng, N.[Nenggan],
HAM: Hybrid attention module in deep convolutional neural networks for image classification,
PR(129), 2022, pp. 108785.
Elsevier DOI 2206
Hybrid attention module, Channel attention map, Spatial feature descriptor, HAM-integrated networks BibRef

Pai, G.[Gautam], Bronstein, A.M.[Alex M.], Talmon, R.[Ronen], Kimmel, R.[Ron],
Deep Isometric Maps,
IVC(123), 2022, pp. 104461.
Elsevier DOI 2206
BibRef
Earlier: A1, A3, A2, A4:
DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling,
WACV19(819-828)
IEEE DOI 1904
Multidimensional scaling, Manifold learning, Non-linear dimensionality reduction, Neural networks. computational geometry, differential geometry, neural nets, sampling methods, unsupervised learning, DIMAL, Interpolation BibRef

Gould, S.[Stephen], Hartley, R.I.[Richard I.], Campbell, D.[Dylan],
Deep Declarative Networks,
PAMI(44), No. 8, August 2022, pp. 3988-4004.
IEEE DOI 2207
Optimization, Deep learning, Mathematical model, Computational modeling, Neural networks, Task analysis, declarative networks BibRef

Tan, L.[Lu], Li, L.[Ling], Liu, W.Q.[Wan-Quan], An, S.J.[Sen-Jian], Munyard, K.[Kylie],
Unsupervised learning of multi-task deep variational model,
JVCIR(87), 2022, pp. 103588.
Elsevier DOI 2208
Unsupervised learning, Integration approach, Deep neural networks, Variational general frameworks, Diverse applications BibRef

Grementieri, L.[Luca], Fioresi, R.[Rita],
Model-Centric Data Manifold: The Data Through the Eyes of the Model,
SIIMS(15), No. 3, 2022, pp. 1140-1156.
DOI Link 2208
BibRef

Zhao, Y.[Yi], Zhang, X.C.[Xin-Chang], Feng, W.M.[Wei-Ming], Xu, J.H.[Jian-Hui],
Deep Learning Classification by ResNet-18 Based on the Real Spectral Dataset from Multispectral Remote Sensing Images,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
Landsat. BibRef

Rezatofighi, H.[Hamid], Zhu, T.Y.[Tian-Yu], Kaskman, R.[Roman], Motlagh, F.T.[Farbod T.], Shi, J.Q.F.[Javen Qin-Feng], Milan, A.[Anton], Cremers, D.[Daniel], Leal-Taixé, L.[Laura], Reid, I.D.[Ian D.],
Learn to Predict Sets Using Feed-Forward Neural Networks,
PAMI(44), No. 12, December 2022, pp. 9011-9025.
IEEE DOI 2212
Deep learning, Object detection, Training, Task analysis, Tensors, CAPTCHAs, Transformers, Random finite set, deep learning BibRef

Ning, X.[Xin], Tian, W.J.[Wei-Juan], He, F.[Feng], Bai, X.[Xiao], Sun, L.[Le], Li, W.J.[Wei-Jun],
Hyper-sausage coverage function neuron model and learning algorithm for image classification,
PR(136), 2023, pp. 109216.
Elsevier DOI 2301
Deep neural networks, Neuron model, Brain-inspired BibRef

Nam, J.H.[Ju-Hyeon], Lee, S.C.[Sang-Chul],
Random image frequency aggregation dropout in image classification for deep convolutional neural networks,
CVIU(232), 2023, pp. 103684.
Elsevier DOI 2305
Deep learning, Convolutional neural network, Image classification, Data augmentation, Frequency domain BibRef

Nam, J.H.[Ju-Hyeon], Lee, S.C.[Sang-Chul],
FSDA: Frequency re-scaling in data augmentation for corruption-robust image classification,
PR(150), 2024, pp. 110332.
Elsevier DOI 2403
Deep learning, Image classification, Convolutional neural networks, Data augmentation, Frequency domain BibRef

Sarang, N.[Nima], Poullis, C.[Charalambos],
Tractable large-scale deep reinforcement learning,
CVIU(232), 2023, pp. 103689.
Elsevier DOI 2305
Deep Reinforcement Learning, Road extraction, Self-supervised learning BibRef

Wang, J.[Jian], Han, Z.W.[Zi-Wei], Jiang, W.J.[Wen-Jing], Kim, J.[Junseok],
A novel classification method combining phase-field and DNN,
PR(142), 2023, pp. 109723.
Elsevier DOI 2307
Phase-field-DNN, Phase-field, DNN, Classification BibRef

Liu, R.S.[Ri-Sheng], Liu, X.[Xuan], Zeng, S.Z.[Shang-Zhi], Zhang, J.[Jin], Zhang, Y.X.[Yi-Xuan],
Hierarchical Optimization-Derived Learning,
PAMI(45), No. 12, December 2023, pp. 14693-14708.
IEEE DOI 2311
BibRef

Si, H.Y.[Hong-Ying], Wei, X.Y.[Xian-Yong],
Feature extraction and representation learning of 3D point cloud data,
IVC(142), 2024, pp. 104890.
Elsevier DOI 2402
Deep learning, 3D data, Point cloud, Represent learning, Feature extraction BibRef

Peck, J.[Jonathan], Goossens, B.[Bart], Saeys, Y.[Yvan],
An Introduction to Adversarially Robust Deep Learning,
PAMI(46), No. 4, April 2024, pp. 2071-2090.
IEEE DOI 2403
Perturbation methods, Deep learning, Surveys, Robustness, Mathematical models, Image recognition, Predictive models, deep learning BibRef

Tai, X.C.[Xue-Cheng], Liu, H.[Hao], Chan, R.[Raymond],
PottsMGNet: A Mathematical Explanation of Encoder-Decoder Based Neural Networks,
SIIMS(17), No. 1, 2024, pp. 540-594.
DOI Link 2404
BibRef

Pakhare, J.D.[Jayamala D.], Uplane, M.D.[Mahadev D.],
Hybrid Mayfly Lévy Flight Distribution Optimization Algorithm-Tuned Deep Convolutional Neural Network for Indoor-Outdoor Image Classification,
IJIG(24), No. 2, March 2024, pp. 2450024.
DOI Link 2404
BibRef

Liu, Q.[Qian], Wang, C.[Cunbao],
Deep network with double reuses and convolutional shortcuts,
IET-CV(18), No. 4, 2024, pp. 512-525.
DOI Link 2406
convolutional neural nets, learning (artificial intelligence) BibRef

Dong, X.P.[Xing-Ping], Ouyang, T.R.[Tian-Ran], Liao, S.C.[Sheng-Cai], Du, B.[Bo], Shao, L.[Ling],
Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning,
IP(33), 2024, pp. 5663-5675.
IEEE DOI Code:
WWW Link. 2410
Training, Data models, Metalearning, Degradation, Accuracy, Smoothing methods, Semisupervised learning, Few-shot learning, pseudo-labeling BibRef

Dong, X.P.[Xing-Ping], Shen, J.B.[Jian-Bing], Shao, L.[Ling],
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning,
ECCV22(XX:169-186).
Springer DOI 2211

WWW Link. BibRef

Liu, S.[Shunyu], Song, J.[Jie], Zhou, Y.[Yihe], Yu, N.[Na], Chen, K.X.[Kai-Xuan], Feng, Z.[Zunlei], Song, M.L.[Ming-Li],
Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning,
PAMI(46), No. 12, December 2024, pp. 8157-8172.
IEEE DOI 2411
Prototypes, Training, Task analysis, Predator prey systems, Noise measurement, History, Observability, interaction pattern disentangling BibRef

Xu, K.J.[Ke-Jian], Chen, J.L.[Jin-Long], Ning, Y.[Yi], Tang, W.[Wei],
Deep Learning in Image Classification: An Overview,
CVIDL23(81-93)
IEEE DOI 2403
Deep learning, Training, Computational modeling, Neural networks, Optimization methods, Transformers, Deep Learning, Transformers BibRef

Luo, J.B.[Jia-Bin], Luo, R.Z.[Rong-Zhen],
Research on Image Recognition based on Reinforcement Learning,
CVIDL23(25-28)
IEEE DOI 2403
Deep learning, Image recognition, Costs, Computational modeling, Neural networks, Reinforcement learning, recognition accuracy BibRef

Panousis, K.P.[Konstantinos P.], Ienco, D.[Dino], Marcos, D.[Diego],
Sparse Linear Concept Discovery Models,
CLVL23(2759-2763)
IEEE DOI Code:
WWW Link. 2401
BibRef

Kim, H.[Hyungmin], Suh, S.[Sungho], Kim, D.[Daehwan], Jeong, D.[Daun], Cho, H.S.[Han-Sang], Kim, J.[Junmo],
Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery,
ICCV23(16642-16651)
IEEE DOI 2401
Novel category BibRef

Qian, Q.[Qi],
Stable Cluster Discrimination for Deep Clustering,
ICCV23(16599-16608)
IEEE DOI 2401
BibRef

Stergiou, A.[Alexandros], Deligiannis, N.[Nikos],
Leaping Into Memories: Space-Time Deep Feature Synthesis,
ICCV23(1966-1976)
IEEE DOI 2401
BibRef

Djenouri, Y.[Youcef], Belbachir, A.N.[Ahmed Nabil], Jhaveri, R.H.[Rutvij H.], Djenouri, D.[Djamel],
Knowledge Guided Deep Learning for General-purpose Computer Vision Applications,
CAIP23(I:185-194).
Springer DOI 2312
BibRef

Yong, H.W.[Hong-Wei], Sun, Y.[Ying], Zhang, L.[Lei],
A General Regret Bound of Preconditioned Gradient Method for DNN Training,
CVPR23(7866-7875)
IEEE DOI 2309
BibRef

Metaxas, I.M.[Ioannis Maniadis], Tzimiropoulos, G.[Georgios], Patras, I.[Ioannis],
DivClust: Controlling Diversity in Deep Clustering,
CVPR23(3418-3428)
IEEE DOI 2309
BibRef

Frey, M.[Markus], Doeller, C.F.[Christian F.], Barry, C.[Caswell],
Probing Neural Representations of Scene Perception in a Hippocampally Dependent Task Using Artificial Neural Networks,
CVPR23(2113-2121)
IEEE DOI 2309
BibRef

Mahapatra, D.[Dwarikanath], Reyes, M.[Mauricio],
Multi-label Attention Map Assisted Deep Feature Learning for Medical Image Classification,
MIA-COVID19D22(722-734).
Springer DOI 2304
BibRef

Hammam, A.[Ahmed], Bonarens, F.[Frank], Ghobadi, S.E.[Seyed Eghbal], Stiller, C.[Christoph],
Towards Improved Intermediate Layer Variational Inference for Uncertainty Estimation,
SafeDrive22(526-542).
Springer DOI 2304
BibRef

Zhang, X.C.[Xian-Chao], Yang, W.T.[Wen-Tao], Zhang, X.T.[Xiao-Tong], Liu, H.[Han], Wang, G.L.[Guang-Lu],
Data-Efficient Deep Reinforcement Learning with Symmetric Consistency,
ICPR22(2430-2436)
IEEE DOI 2212
Deep learning, Training, Perturbation methods, Semantics, Supervised learning, Estimation, Reinforcement learning BibRef

Egele, R.[Romain], Maulik, R.[Romit], Raghavan, K.[Krishnan], Lusch, B.[Bethany], Guyon, I.[Isabelle], Balaprakash, P.[Prasanna],
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification,
ICPR22(1908-1914)
IEEE DOI 2212
Ensemble to model uncertainty. Deep learning, Training, Uncertainty, Scalability, Neural networks, Predictive models BibRef

Subia-Waud, C.[Christopher], Dasmahapatra, S.[Srinandan],
Weight Fixing Networks,
ECCV22(XI:415-431).
Springer DOI 2211

WWW Link. BibRef

Guo, J.[Jun], Chen, Y.H.[Yong-Hong], Hao, Y.H.[Yi-Hang], Yin, Z.X.[Zi-Xin], Yu, Y.[Yin], Li, S.[Simin],
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning,
ArtOfRobust22(114-121)
IEEE DOI 2210
Degradation, Deep learning, Power system management, Neural networks, Reinforcement learning, Markov processes, Robustness BibRef

Zhu, L.[Lei], She, Q.[Qi], Li, D.[Duo], Lu, Y.[Yanye], Kang, X.J.[Xue-Jing], Hu, J.[Jie], Wang, C.H.[Chang-Hu],
Unifying Nonlocal Blocks for Neural Networks,
ICCV21(12272-12281)
IEEE DOI 2203
Deep learning, Image segmentation, Image recognition, Neural networks, Semantics, Information filters, Video analysis and understanding BibRef

Vasconcelos, C.[Cristina], Larochelle, H.[Hugo], Dumoulin, V.[Vincent], Romijnders, R.[Rob], Roux, N.L.[Nicolas Le], Goroshin, R.[Ross],
Impact of Aliasing on Generalization in Deep Convolutional Networks,
ICCV21(10509-10518)
IEEE DOI 2203
Convolutional codes, Art, Convolution, Low-pass filters, Performance gain, Representation learning, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Huang, S.H.[Shi-Hua], Lu, Z.C.[Zhi-Chao], Cheng, R.[Ran], He, C.[Cheng],
FaPN: Feature-aligned Pyramid Network for Dense Image Prediction,
ICCV21(844-853)
IEEE DOI 2203
Code, Deep Learning.
WWW Link. Deep learning, Image segmentation, Codes, Neural networks, Feature extraction, grouping and shape BibRef

Lengyel, A.[Attila], van Gemert, J.C.[Jan C.],
Exploiting Learned Symmetries in Group Equivariant Convolutions,
ICIP21(759-763)
IEEE DOI 2201
Convolutional codes, Deep learning, Image processing, Convolutional neural networks, group equivariant convolutions, efficient deep learning BibRef

Mdrafi, R.[Robiulhossain], Gurbuz, A.C.[Ali Cafer],
Compressed Classification from Learned Measurements,
LCI21(4021-4030)
IEEE DOI 2112
Classification form compressive sensed data. Weight measurement, Deep learning, Image coding, Loss measurement, Robustness BibRef

Huang, L.[Lei], Zhou, Y.[Yi], Liu, L.[Li], Zhu, F.[Fan], Shao, L.[Ling],
Group Whitening: Balancing Learning Efficiency and Representational Capacity,
CVPR21(9507-9516)
IEEE DOI 2111
Deep learning, Analytical models, Sociology, Standardization, Benchmark testing BibRef

Pestana, C.[Camilo], Liu, W.[Wei], Glance, D.[David], Owens, R.[Robyn], Mian, A.[Ajmal],
Assistive Signals for Deep Neural Network Classifiers,
LXCV21(1221-1225)
IEEE DOI 2109
Deep learning, Perturbation methods, Optimization methods, Lighting BibRef

Ding, Y.F.[Yi-Fan], Wang, L.Q.[Li-Qiang], Gong, B.Q.[Bo-Qing],
Analyzing Deep Neural Network's Transferability via Fréchet Distance,
WACV21(3931-3940)
IEEE DOI 2106
Measurement, Degradation, Training, Correlation, Transfer learning, Neural networks BibRef

Jamadandi, A.[Adarsh], Tigadoli, R.[Rishabh], Tabib, R.[Ramesh], Mudenagudi, U.[Uma],
Probabilistic Word Embeddings in Kinematic Space,
ICPR21(8759-8765)
IEEE DOI 2105
Geometry, Uncertainty, Computational modeling, Kinematics, Transforms, Aerospace electronics, Tools BibRef

Shiran, G.[Guy], Weinshall, D.[Daphna],
Multi-Modal Deep Clustering: Unsupervised Partitioning of Images,
ICPR21(4728-4735)
IEEE DOI 2105
Deep learning, Neural networks, Image representation, Benchmark testing, Task analysis, Gaussian mixture model BibRef

Takenaga, S.[Shintaro], Watanabe, S.[Shuhei], Nomura, M.[Masahiro], Ozaki, Y.[Yoshihiko], Onishi, M.[Masaki], Habe, H.[Hitoshi],
Evaluating Initialization of Nelder-Mead Method for Hyperparameter Optimization in Deep Learning,
ICPR21(3372-3379)
IEEE DOI 2105
Deep learning, Shape, Optimization BibRef

Georgiou, T.[Theodoros], Schmitt, S.[Sebastian], Bäck, T.[Thomas], Pu, N.[Nan], Chen, W.[Wei], Lew, M.[Michael],
Comparison of deep learning and hand crafted features for mining simulation data,
ICPR21(1-8)
IEEE DOI 2105
Deep learning, Solid modeling, Dictionaries, Computational modeling, Detectors BibRef

Poyser, M.[Matt], Atapour-Abarghouei, A.[Amir], Breckon, T.P.[Toby P.],
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures,
ICPR21(2830-2837)
IEEE DOI 2105
Performance evaluation, Image segmentation, Image coding, Pose estimation, Transform coding, Network architecture, Video compression BibRef

Jie, R.L.[Ren-Long], Gao, J.B.[Jun-Bin], Vasnev, A.[Andrey], Tran, M.N.[Minh-Ngoc],
Regularized Flexible Activation Function Combination for Deep Neural Networks,
ICPR21(2001-2008)
IEEE DOI 2105
Convolutional codes, Image coding, Time series analysis, Neural networks, Stability criteria, Predictive models BibRef

Goncalves do Santos, C.F.[Claudio Filipi], Colombo, D.[Danilo], Roder, M.[Mateus], Papa, J.P.[João Paulo],
MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values,
ICPR21(2671-2676)
IEEE DOI 2105
Deep learning, Neurons, Turning, Convolutional neural networks, Biological neural networks, Image classification BibRef

You, J., Korhonen, J.,
Attention Boosted Deep Networks For Video Classification,
ICIP20(1761-1765)
IEEE DOI 2011
Feature extraction, Video sequences, video classification BibRef

Zhang, X.[Xiao], Zhao, R.[Rui], Qiao, Y.[Yu], Li, H.S.[Hong-Sheng],
RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis Function Softmax,
ECCV20(XXVI:296-311).
Springer DOI 2011
Code, RBF.
WWW Link. BibRef

Chen, Y.P.[Yin-Peng], Dai, X.Y.[Xi-Yang], Liu, M.C.[Meng-Chen], Chen, D.D.[Dong-Dong], Yuan, L.[Lu], Liu, Z.C.[Zi-Cheng],
Dynamic ReLU,
ECCV20(XIX:351-367).
Springer DOI 2011
Rectified linear units BibRef

Reimers, C.[Christian], Runge, J.[Jakob], Denzler, J.[Joachim],
Determining the Relevance of Features for Deep Neural Networks,
ECCV20(XXVI:330-346).
Springer DOI 2011
BibRef

Zhao, J.J.[Jun-Jie], Lu, D.H.[Dong-Huan], Ma, K.[Kai], Zhang, Y.[Yu], Zheng, Y.F.[Ye-Feng],
Deep Image Clustering with Category-style Representation,
ECCV20(XIV:54-70).
Springer DOI 2011

WWW Link. BibRef

Chen, D.D.[Dong-Dong], Davies, M.E.[Mike E.],
Deep Decomposition Learning for Inverse Imaging Problems,
ECCV20(XXVIII:510-526).
Springer DOI 2011
Code, DNN.
WWW Link. BibRef

Huang, L.[Lei], Qin, J.[Jie], Liu, L.[Li], Zhu, F.[Fan], Shao, L.[Ling],
Layer-wise Conditioning Analysis in Exploring the Learning Dynamics of DNNs,
ECCV20(II:384-401).
Springer DOI 2011
BibRef

Li, D.[Duo], Chen, Q.F.[Qi-Feng],
Deep Reinforced Attention Learning for Quality-Aware Visual Recognition,
ECCV20(XVI: 493-509).
Springer DOI 2010
BibRef

Yong, H.W.[Hong-Wei], Huang, J.Q.[Jian-Qiang], Meng, D.Y.[De-Yu], Hua, X.S.[Xian-Sheng], Zhang, L.[Lei],
Momentum Batch Normalization for Deep Learning with Small Batch Size,
ECCV20(XII: 224-240).
Springer DOI 2010
BibRef

Gustafsson, F.K., Danelljan, M., Schon, T.B.,
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision,
SAIAD20(1289-1298)
IEEE DOI 2008
Uncertainty, Task analysis, Estimation, Predictive models, Bayes methods, Machine learning BibRef

Le, E., Kokkinos, I., Mitra, N.J.,
Going Deeper With Lean Point Networks,
CVPR20(9500-9509)
IEEE DOI 2008
Convolution, Memory management, Training BibRef

Zhang, X., Qin, S., Xu, Y., Xu, H.,
Quaternion Product Units for Deep Learning on 3D Rotation Groups,
CVPR20(7302-7311)
IEEE DOI 2008
Quaternions, Robustness, Skeleton, Algebra, Data models, Machine learning BibRef

Song, J., Chen, Y., Ye, J., Wang, X., Shen, C., Mao, F., Song, M.,
DEPARA: Deep Attribution Graph for Deep Knowledge Transferability,
CVPR20(3921-3929)
IEEE DOI 2008
Task analysis, Computational modeling, Feature extraction, Data models, Dictionaries, Probes, Computer architecture BibRef

Wu, R.[Rundi], Zhuang, Y.X.[Yi-Xin], Xu, K.[Kai], Zhang, H.[Hao], Chen, B.Q.[Bao-Quan],
PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes,
CVPR20(826-835)
IEEE DOI 2008
Shape, Geometry, Solid modeling, Decoding, Adaptation models, Neural networks BibRef

Lee, E., Lee, C.,
NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks,
CVPR20(1475-1484)
IEEE DOI 2008
Neurons, Biological neural networks, Iterative methods, Redundancy, Taylor series, Computational efficiency BibRef

Gao, S., Huang, F., Pei, J., Huang, H.,
Discrete Model Compression With Resource Constraint for Deep Neural Networks,
CVPR20(1896-1905)
IEEE DOI 2008
Logic gates, Computational modeling, Stochastic processes, Neural networks, Training, Computational efficiency, Acceleration BibRef

Nan, Y., Ji, H.,
Deep Learning for Handling Kernel/model Uncertainty in Image Deconvolution,
CVPR20(2385-2394)
IEEE DOI 2008
Kernel, Deconvolution, Image restoration, Convolution, Artificial neural networks, Robustness, Optimization BibRef

Singh, S.[Saurabh], Shrivastava, A.[Abhinav],
EvalNorm: Estimating Batch Normalization Statistics for Evaluation,
ICCV19(3632-3640)
IEEE DOI 2004
learning (artificial intelligence), object detection, batch normalization statistics, deep learning, peculiar behavior, Google BibRef

Huang, S.Y.[Shuai-Yi], Wang, Q.Y.[Qiu-Yue], Zhang, S.Y.[Song-Yang], Yan, S.P.[Shi-Peng], He, X.M.[Xu-Ming],
Dynamic Context Correspondence Network for Semantic Alignment,
ICCV19(2010-2019)
IEEE DOI 2004
image fusion, image representation, Pattern matching, supervised learning, dynamic context correspondence network. BibRef

Maximov, M., Ritschel, T., Leal-Taixé, L., Fritz, M.,
Deep Appearance Maps,
ICCV19(8728-8737)
IEEE DOI 2004
gradient methods, image reconstruction, image representation, image segmentation, learning (artificial intelligence), lighting, Image color analysis BibRef

Wu, J., Long, K., Wang, F., Qian, C., Li, C., Lin, Z., Zha, H.,
Deep Comprehensive Correlation Mining for Image Clustering,
ICCV19(8149-8158)
IEEE DOI 2004
data mining, feature extraction, image representation, pattern clustering, unsupervised learning, Task analysis BibRef

Li, G., Müller, M., Thabet, A., Ghanem, B.,
DeepGCNs: Can GCNs Go As Deep As CNNs?,
ICCV19(9266-9275)
IEEE DOI 2004
convolutional neural nets, graph theory, image segmentation, learning (artificial intelligence), solid modelling, Stacking BibRef

Fong, R., Patrick, M., Vedaldi, A.,
Understanding Deep Networks via Extremal Perturbations and Smooth Masks,
ICCV19(2950-2958)
IEEE DOI 2004
image representation, neural nets, optimisation, smoothing methods, smooth masks, deep neural network, optimization problem BibRef

Pan, X., Zhan, X., Shi, J., Tang, X., Luo, P.,
Switchable Whitening for Deep Representation Learning,
ICCV19(1863-1871)
IEEE DOI 2004
convolutional neural nets, image representation, image segmentation, learning (artificial intelligence), Semantics BibRef

Hernández-Garcia, A., König, P.,
Learning Representational Invariance Instead of Categorization,
Preregister19(4587-4590)
IEEE DOI 2004
image classification, learning (artificial intelligence), neural nets, object recognition, adversarial vulnerability, deep learning BibRef

Wang, C.Y.[Chien-Yao], Liao, H.Y.M.[Hong-Yuan Mark], Chen, P.Y.[Ping-Yang], Hsieh, J.W.[Jun-Wei],
Enriching Variety of Layer-Wise Learning Information by Gradient Combination,
LPCV19(2477-2484)
IEEE DOI 2004
feature extraction, image recognition, image segmentation, learning (artificial intelligence), object detection, object detection BibRef

Chen, H., Lin, M., Sun, X., Qi, Q., Li, H., Jin, R.,
MuffNet: Multi-Layer Feature Federation for Mobile Deep Learning,
CEFRL19(2943-2952)
IEEE DOI 2004
convolutional neural nets, image classification, image representation, learning (artificial intelligence), convolution network BibRef

Choi, J., Seo, H., Im, S., Kang, M.,
Attention Routing Between Capsules,
NeruArch19(1981-1989)
IEEE DOI 2004
affine transforms, feature extraction, image classification, learning (artificial intelligence), multilayer perceptrons, Deep learning BibRef

Durand, T.[Thibaut], Mehrasa, N.[Nazanin], Mori, G.[Greg],
Learning a Deep ConvNet for Multi-Label Classification With Partial Labels,
CVPR19(647-657).
IEEE DOI 2002
BibRef

Hossain, M.T.[Md Tahmid], Teng, S.W.[Shyh Wei], Zhang, D.S.[Deng-Sheng], Lim, S.[Suryani], Lu, G.J.[Guo-Jun],
Distortion Robust Image Classification Using Deep Convolutional Neural Network with Discrete Cosine Transform,
ICIP19(659-663)
IEEE DOI 1910
CNN, DCT, Dropout, Distortion, VGG16 BibRef

Arroyo, R.[Roberto], Tovar, J.[Javier], Delgado, F.J.[Francisco J.], Almazán, E.J.[Emilio J.], Serrador, D.G.[Diego G.], Hurtado, A.[Antonio],
Deep Learning of Visual and Textual Data for Region Detection Applied to Item Coding,
IbPRIA19(I:329-341).
Springer DOI 1910
Using text on the image. BibRef

Onchis, D.M.[Darian M.], Istin, C.[Codruta], Real, P.[Pedro],
Refined Deep Learning for Digital Objects Recognition via Betti Invariants,
CAIP19(I:613-621).
Springer DOI 1909
BibRef

Lan, X.[Xu], Zhu, X.T.[Xia-Tian], Gong, S.G.[Shao-Gang],
Self-Referenced Deep Learning,
ACCV18(II:284-300).
Springer DOI 1906
BibRef

Hinterstoisser, S.[Stefan], Lepetit, V.[Vincent], Wohlhart, P.[Paul], Konolige, K.[Kurt],
On Pre-trained Image Features and Synthetic Images for Deep Learning,
4DPose18(I:682-697).
Springer DOI 1905
BibRef

Zhang, H.[Huan], Shi, H.[Hong], Wang, W.W.[Wen-Wu],
Cascade Deep Networks for Sparse Linear Inverse Problems,
ICPR18(812-817)
IEEE DOI 1812
Inverse problems, Linear programming, Image resolution, Signal resolution, Convergence, Time complexity BibRef

Yang, Y.Q.[Yao-Qing], Feng, C.[Chen], Shen, Y.[Yiru], Tian, D.[Dong],
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation,
CVPR18(206-215)
IEEE DOI 1812
Decoding, Image reconstruction, Surface reconstruction, Neural networks BibRef

Caron, M.[Mathilde], Bojanowski, P.[Piotr], Joulin, A.[Armand], Douze, M.[Matthijs],
Deep Clustering for Unsupervised Learning of Visual Features,
ECCV18(XIV: 139-156).
Springer DOI 1810
BibRef

Xu, Y.F.[Yi-Fan], Fan, T.Q.[Tian-Qi], Xu, M.Y.[Ming-Ye], Zeng, L.[Long], Qiao, Y.[Yu],
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters,
ECCV18(VIII: 90-105).
Springer DOI 1810
BibRef

Wang, Q.[Qiang], Xu, J.Q.[Jia-Qing], Li, R.C.[Rong-Chun], Qiao, P.[Peng], Yang, K.[Ke], Li, S.J.[Shi-Jie], Dou, Y.[Yong],
Deep Image Clustering Using Convolutional Autoencoder Embedding with Inception-Like Block,
ICIP18(2356-2360)
IEEE DOI 1809
Convolutional codes, Convolution, Clustering algorithms, Image reconstruction, Decoding, Clustering methods, Task analysis, Kullback-Leibler divergence BibRef

Smith, K.E.[Kaleb E.], Williams, P.[Phillip], Chaiya, T.[Tatsanee], Ble, M.[Max],
Deep Convolutional-Shepard Interpolation Neural Networks for Image Classification Tasks,
ICIAR18(185-192).
Springer DOI 1807
BibRef

Wu, J., Qiu, S., Kong, Y., Chen, Y., Senhadji, L., Shu, H.,
MomentsNet: A simple learning-free method for binary image recognition,
ICIP17(2667-2671)
IEEE DOI 1803
Backpropagation, Feature extraction, Histograms, Image recognition, Machine learning, Transforms, Deep learning, MomentsNet, convolutional neural network BibRef

Dizaji, K.G.[Kamran Ghasedi], Herandi, A.[Amirhossein], Deng, C.[Cheng], Cai, W.D.[Wei-Dong], Huang, H.[Heng],
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization,
ICCV17(5747-5756)
IEEE DOI 1802
data visualisation, entropy, estimation theory, learning (artificial intelligence), minimisation, Tuning BibRef

Sun, C.[Chen], Shrivastava, A.[Abhinav], Singh, S.[Saurabh], Gupta, A.[Abhinav],
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era,
ICCV17(843-852)
IEEE DOI 1802
With a very large dataset. learning (artificial intelligence), pose estimation, JFT-300M dataset, base model, dataset size, Visualization BibRef

Park, E.[Eunhyeok], Ahn, J.[Junwhan], Yoo, S.[Sungjoo],
Weighted-Entropy-Based Quantization for Deep Neural Networks,
CVPR17(7197-7205)
IEEE DOI 1711
Computational modeling, Embedded systems, Entropy, Hardware, Mobile communication, Neural networks, Quantization, (signal) BibRef

Worrall, D.E.[Daniel E.], Garbin, S.J.[Stephan J.], Turmukhambetov, D.[Daniyar], Brostow, G.J.[Gabriel J.],
Harmonic Networks: Deep Translation and Rotation Equivariance,
CVPR17(7168-7177)
IEEE DOI 1711
To deal with rotations. Detectors, Filtering theory, Harmonic analysis, Maximum likelihood detection, Nonlinear filters, Power, harmonic, filters BibRef

Yang, X., Ramesh, P., Chitta, R., Madhvanath, S., Bernal, E.A., Luo, J.,
Deep Multimodal Representation Learning from Temporal Data,
CVPR17(5066-5074)
IEEE DOI 1711
Correlation, Data models, Decoding, Fuses, Machine learning, Robustness BibRef

Guo, Y.W.[Yi-Wen], Yao, A.B.[An-Bang], Zhao, H.[Hao], Chen, Y.R.[Yu-Rong],
Network Sketching: Exploiting Binary Structure in Deep CNNs,
CVPR17(4040-4048)
IEEE DOI 1711
Approximation algorithms, Computational modeling, Mathematical model, Memory management, Tensile, stress BibRef

Diba, A.[Ali], Sharma, V.[Vivek], Van Gool, L.J.[Luc J.],
Deep Temporal Linear Encoding Networks,
CVPR17(1541-1550)
IEEE DOI 1711
Computational modeling, Encoding, Optical fiber networks, Optical imaging, Robustness, Videos BibRef

Achsas, S., Nfaoui, E.H.,
Improving relational aggregated search from big data sources using deep learning,
ISCV17(1-6)
IEEE DOI 1710
Data mining, Feature extraction, Information retrieval, Neural networks, Big Data Sources, Deep Learning, Information Extraction, Information nuggets, Knowledge bases, Relational Aggregated Search, Stacked, Autoencoders BibRef

Dupre, R., Tzimiropoulos, G., Argyriou, V.,
Automated Risk Assessment for Scene Understanding and Domestic Robots Using RGB-D Data and 2.5D CNNs at a Patch Level,
DeepLearnRV17(476-477)
IEEE DOI 1709
Labeling, Machine learning, Shape, BibRef

Bentes Gatto, B.[Bernardo], de Souza, L.S.[Lincon Sales], dos Santos, E.M.[Eulanda M.],
A deep network model based on subspaces: A novel approach for image classification,
MVA17(436-439)
DOI Link 1708
Discrete cosine transforms, Face, Face recognition, Machine learning, Neural networks, Principal component analysis BibRef

Mojoo, J.[Jonathan], Kurosawa, K.[Keiichi], Kurita, T.[Takio],
Deep CNN with Graph Laplacian Regularization for Multi-label Image Annotation,
ICIAR17(19-26).
Springer DOI 1706
BibRef

McCane, B., Szymanskic, L.,
Deep networks are efficient for circular manifolds,
ICPR16(3464-3469)
IEEE DOI 1705
Geometry, Logic gates, Manifolds, Neural networks, Neurons, Pattern recognition. BibRef

Zhao, Z.B.[Zhen-Bing], Xu, G.Z.[Guo-Zhi], Qi, Y.C.[Yin-Cheng],
Multi-Scale Hierarchy Deep Feature Aggregation for Compact Image Representations,
DeepVisual16(III: 557-571).
Springer DOI 1704
BibRef

Krutsch, R., Naidu, S.,
Monte Carlo method based precision analysis of deep convolution nets,
DASIP16(162-167)
IEEE DOI 1704
Monte Carlo methods BibRef

Yu, T.Y.[Tian-Yuan], Bai, L.[Liang], Guo, J.L.[Jin-Lin], Yang, Z.[Zheng], Xie, Y.X.[Yu-Xiang],
Deep Convolutional Neural Network for Bidirectional Image-Sentence Mapping,
MMMod17(II: 136-147).
Springer DOI 1701
BibRef

Islam, M.A.[M. Amirul], Rochan, M., Bruce, N.D.B.[Neil D.B.], Wang, Y.[Yang],
Gated Feedback Refinement Network for Dense Image Labeling,
CVPR17(4877-4885)
IEEE DOI 1711
BibRef
Earlier: A1, A3, A4, Only:
Dense Image Labeling Using Deep Convolutional Neural Networks,
CRV16(16-23)
IEEE DOI 1612
Convolution, Decoding, Encoding, Labeling, Logic gates, Semantics, Spatial resolution. Deep Convolutional Neural Network BibRef

Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.B.,
Learning Deep Features for Discriminative Localization,
CVPR16(2921-2929)
IEEE DOI 1612
BibRef

Murdock, C., Li, Z., Zhou, H., Duerig, T.,
Blockout: Dynamic Model Selection for Hierarchical Deep Networks,
CVPR16(2583-2591)
IEEE DOI 1612
BibRef

Kalantidis, Y.[Yannis], Mellina, C.[Clayton], Osindero, S.[Simon],
Cross-Dimensional Weighting for Aggregated Deep Convolutional Features,
WebScale16(I: 685-701).
Springer DOI 1611
BibRef

Papadopoulos, G.T.[Georgios T.], Machairidou, E.[Elpida], Daras, P.[Petros],
Deep cross-layer activation features for visual recognition,
ICIP16(923-927)
IEEE DOI 1610
Correlation. Last layer of the CNN may not capture every scale of feature. BibRef

Qi, M.S.[Meng-Shi], Wang, Y.H.[Yun-Hong],
DEEP-CSSR: Scene classification using category-specific salient region with deep features,
ICIP16(1047-1051)
IEEE DOI 1610
Bio inspired models. BibRef

Anantrasirichai, N., Gilchrist, I.D., Bull, D.R.,
Visual salience and priority estimation for locomotion using a deep convolutional neural network,
ICIP16(1599-1603)
IEEE DOI 1610
Estimation BibRef

Chaabouni, S., Benois-Pineau, J., Ben Amar, C.,
Transfer learning with deep networks for saliency prediction in natural video,
ICIP16(1604-1608)
IEEE DOI 1610
Benchmark testing BibRef

Makantasis, K., Doulamis, A., Doulamis, N., Psychas, K.,
Deep learning based human behavior recognition in industrial workflows,
ICIP16(1609-1613)
IEEE DOI 1610
Computer architecture BibRef

Gaur, U., Kourakis, M., Newman-Smith, E., Smith, W., Manjunath, B.S.,
Membrane segmentation via active learning with deep networks,
ICIP16(1943-1947)
IEEE DOI 1610
Computer architecture BibRef

Porter, R.B., Zimmer, B.G.,
Deep segmentation networks using 'simple' multi-layered graphical models,
Southwest16(41-44)
IEEE DOI 1605
Feeds BibRef

Hiranandani, G., Karnick, H.,
Improved Classification and Reconstruction by Introducing Independence and Randomization in Deep Neural Networks,
DICTA15(1-8)
IEEE DOI 1603
image classification BibRef

Ueki, K., Kobayashi, T.,
Multi-layer feature extractions for image classification: Knowledge from deep CNNs,
WSSIP15(9-12)
IEEE DOI 1603
BibRef
And: WSSIP15(9-12)
IEEE DOI 1603
feature extraction. Computer vision 2 papers listed. BibRef

Ba, J.L.[Jimmy Lei], Swersky, K.[Kevin], Fidler, S.[Sanja], Salakhutdinov, R.[Ruslan],
Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions,
ICCV15(4247-4255)
IEEE DOI 1602
Electronic publishing BibRef

Huang, J.J.[Jia-Ji], Qiu, Q.[Qiang], Calderbank, R.[Robert], Sapiro, G.[Guillermo],
Geometry-Aware Deep Transform,
ICCV15(4139-4147)
IEEE DOI 1602
Machine learning. Use geometry. BibRef

Aubry, M.[Mathieu], Russell, B.C.[Bryan C.],
Understanding Deep Features with Computer-Generated Imagery,
ICCV15(2875-2883)
IEEE DOI 1602
Computational modeling BibRef

Cheng, Y., Yu, F.X., Feris, R.S., Kumar, S., Choudhary, A., Chang, S.F.,
An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections,
ICCV15(2857-2865)
IEEE DOI 1602
Complexity theory BibRef

Feng, J., Darrell, T.J.,
Learning the Structure of Deep Convolutional Networks,
ICCV15(2749-2757)
IEEE DOI 1602
Adaptation models BibRef

Wu, R., Wang, B., Wang, W., Yu, Y.,
Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification,
ICCV15(1287-1295)
IEEE DOI 1602
Aggregates BibRef

Yan, Z., Zhang, H., Piramuthu, R., Jagadeesh, V., de Coste, D., Di, W., Yu, Y.,
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition,
ICCV15(2740-2748)
IEEE DOI 1602
Computer architecture BibRef

Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., Moreno-Noguer, F.,
Discriminative Learning of Deep Convolutional Feature Point Descriptors,
ICCV15(118-126)
IEEE DOI 1602
Computational modeling BibRef

Monti, F., Boscaini, D., Masci, J., Rodolà, E., Svoboda, J., Bronstein, M.M.[Michael M.],
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs,
CVPR17(5425-5434)
IEEE DOI 1711
Computational modeling, Convolution, Laplace equations, Machine learning, Manifolds, Shape, BibRef

Gordo, A.[Albert], Gaidon, A.[Adrien], Perronnin, F.[Florent],
Deep Fishing: Gradient Features from Deep Nets,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Thewlis, J.[James], Zheng, S.[Shuai], Torr, P.H.S.[Philip H.S.], Vedaldi, A.[Andrea],
Fully-trainable deep matching,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Bilen, H., Vedaldi, A.,
Weakly Supervised Deep Detection Networks,
CVPR16(2846-2854)
IEEE DOI 1612
BibRef

Nguyen, K.[Kien], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Deep Context Modeling for Semantic Segmentation,
WACV17(56-63)
IEEE DOI 1609
BibRef
Earlier:
Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling,
ICIP16(1344-1348)
IEEE DOI 1610
BibRef
Earlier:
Improving deep convolutional neural networks with unsupervised feature learning,
ICIP15(2270-2274)
IEEE DOI 1512
Feature extraction, Graphical models, Image segmentation, Kernel, Labeling, Neural networks, Semantics, context modeling, scene parsing, scene understanding, semantic segmentation. Computational modeling. Convolutional Neural Network BibRef

Talathi, S.S.[Sachin S.],
Hyper-parameter optimization of deep convolutional networks for object recognition,
ICIP15(3982-3986)
IEEE DOI 1512
deep convolution networks BibRef

Yamashita, T.[Takayoshi], Tanaka, M.[Masayuki], Yamauchi, Y.[Yuji], Fujiyoshi, H.[Hironobu],
SWAP-NODE: A regularization approach for deep convolutional neural networks,
ICIP15(2475-2479)
IEEE DOI 1512
deep learning; dropout; regularization; swap-node BibRef

Afzal, M.Z.[Muhammad Zeshan], Capobianco, S.[Samuele], Malik, M.I.[Muhammad Imran], Marinai, S.[Simone], Breuel, T.M.[Thomas M.], Dengel, A.[Andreas], Liwicki, M.[Marcus],
Deepdocclassifier: Document classification with deep Convolutional Neural Network,
ICDAR15(1111-1115)
IEEE DOI 1511
Convolutional Neural Network;Deep CNN;Document Image Classification BibRef

Christodoulidis, S.[Stergios], Anthimopoulos, M.[Marios], Mougiakakou, S.[Stavroula],
Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks,
MADiMa15(458-465).
Springer DOI 1511
BibRef

Li, Y.[Yao], Liu, L.Q.[Ling-Qiao], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Mid-level deep pattern mining,
CVPR15(971-980)
IEEE DOI 1510
Convolutional Neural Networks. BibRef

Amthor, M.[Manuel], Rodner, E.[Erik], Denzler, J.[Joachim],
Impatient DNNs: Deep Neural Networks with Dynamic Time Budgets,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Simon, M.[Marcel], Rodner, E.[Erik],
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks,
ICCV15(1143-1151)
IEEE DOI 1602
Birds BibRef

Denzler, J.[Joachim], Rodner, E.[Erik], Simon, M.[Marcel],
Convolutional Neural Networks as a Computational Model for the Underlying Processes of Aesthetics Perception,
CVAA16(I: 871-887).
Springer DOI 1611
BibRef

Simon, M.[Marcel], Rodner, E.[Erik], Denzler, J.[Joachim],
Part Detector Discovery in Deep Convolutional Neural Networks,
ACCV14(II: 162-177).
Springer DOI 1504
BibRef

Ng, J.Y.H.[Joe Yue-Hei], Hausknecht, M.[Matthew], Vijayanarasimhan, S.[Sudheendra], Vinyals, O.[Oriol], Monga, R.[Rajat], Toderici, G.[George],
Beyond short snippets: Deep networks for video classification,
CVPR15(4694-4702)
IEEE DOI 1510
BibRef

Chen, G.B.[Guo-Bin], Han, T.X.[Tony X.], He, Z.H.[Zhi-Hai], Kays, R.[Roland], Forrester, T.[Tavis],
Deep convolutional neural network based species recognition for wild animal monitoring,
ICIP14(858-862)
IEEE DOI 1502
Birds BibRef

Hafemann, L.G.[Luiz G.], Oliveira, L.S.[Luiz S.], Cavalin, P.[Paulo],
Forest Species Recognition Using Deep Convolutional Neural Networks,
ICPR14(1103-1107)
IEEE DOI 1412
Accuracy BibRef

Gatta, C.[Carlo], Romero, A.[Adriana], van de Veijer, J.[Joost],
Unrolling Loopy Top-Down Semantic Feedback in Convolutional Deep Networks,
DeepLearn14(504-511)
IEEE DOI 1409
BibRef

Yin, X.C.[Xu-Cheng], Yang, C.[Chun], Pei, W.Y.[Wei-Yi], Hao, H.W.[Hong-Wei],
Shallow Classification or Deep Learning: An Experimental Study,
ICPR14(1904-1909)
IEEE DOI 1412
Character recognition BibRef

Kekec, T.[Taygun], Emonet, R.[Remi], Fromont, E.[Elisa], Tremeau, A.[Alain], Wolf, C.[Christian],
Contextually Constrained Deep Networks for Scene Labeling,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Zhong, S.H.[Sheng-Hua], Liu, Y.[Yan], Chung, F.L.[Fu-Lai], Wu, G.S.[Gang-Shan],
Semiconducting bilinear deep learning for incomplete image recognition,
ICMR12(32).
DOI Link 1301
semiconducting bilinear deep belief networks (SBDBN) human's visual cortex. BibRef

Ciregan, D.[Dan], Meier, U.[Ueli], Schmidhuber, J.[Jurgen],
Multi-column deep neural networks for image classification,
CVPR12(3642-3649).
IEEE DOI 1208
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Deep Network Training, Learning, Strategy, Design, Techniques .