Geng, J.,
Wang, H.,
Fan, J.,
Ma, X.,
Deep Supervised and Contractive Neural Network for SAR Image
Classification,
GeoRS(55), No. 4, April 2017, pp. 2442-2459.
IEEE DOI
1704
feature extraction
BibRef
Geng, J.,
Wang, H.,
Fan, J.,
Ma, X.,
SAR Image Classification via Deep Recurrent Encoding Neural Networks,
GeoRS(56), No. 4, April 2018, pp. 2255-2269.
IEEE DOI
1804
Artificial neural networks, Feature extraction, Logic gates,
Machine learning, Radar imaging, Synthetic aperture radar,
synthetic aperture radar (SAR) image
BibRef
Zhang, Z.,
Wang, H.,
Xu, F.,
Jin, Y.Q.,
Complex-Valued Convolutional Neural Network and Its Application in
Polarimetric SAR Image Classification,
GeoRS(55), No. 12, December 2017, pp. 7177-7188.
IEEE DOI
1712
Convolution, Feature extraction, Machine learning,
Neural networks, Synthetic aperture radar, Training,
terrain classification
BibRef
Cheng, Y.,
Wang, D.,
Zhou, P.,
Zhang, T.,
Model Compression and Acceleration for Deep Neural Networks:
The Principles, Progress, and Challenges,
SPMag(35), No. 1, January 2018, pp. 126-136.
IEEE DOI
1801
Computational modeling, Convolution, Convolutional codes,
Machine learning, Neural networks, Quantization (signal), Training data
BibRef
Liang, P.[Peng],
Shi, W.Z.[Wen-Zhong],
Zhang, X.K.[Xiao-Kang],
Remote Sensing Image Classification Based on Stacked Denoising
Autoencoder,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link
1802
Train then add noise and train.
BibRef
Peng, F.F.[Fei-Fei],
Lu, W.[Wei],
Tan, W.X.[Wen-Xia],
Qi, K.L.[Kun-Lun],
Zhang, X.K.[Xiao-Kang],
Zhu, Q.S.[Quan-Sheng],
Multi-Output Network Combining GNN and CNN for Remote Sensing Scene
Classification,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link
2204
BibRef
Li, X.[Xin],
Jie, Z.Q.[Ze-Qun],
Feng, J.S.[Jia-Shi],
Liu, C.S.[Chang-Song],
Yan, S.C.[Shui-Cheng],
Learning with rethinking: Recurrently improving convolutional neural
networks through feedback,
PR(79), 2018, pp. 183-194.
Elsevier DOI
1804
Convolutional neural network, Image classification, Deep learning
BibRef
Yang, Y.,
Wu, Q.M.J.[Qing-Ming Jonathan],
Feng, X.,
Akilan, T.[Thangarajah],
Recomputation of the Dense Layers for Performance Improvement of DCNN,
PAMI(42), No. 11, November 2020, pp. 2912-2925.
IEEE DOI
2010
Training, Mathematical model, Optimization, Neurons,
Convolutional neural networks, Deep learning,
deep learning
BibRef
Yin, P.,
Zhang, S.,
Lyu, J.,
Osher, S.,
Qi, Y.,
Xin, J.,
BinaryRelax: A Relaxation Approach for Training Deep Neural Networks
with Quantized Weights,
SIIMS(11), No. 4, 2018, pp. 2205-2223.
DOI Link
1901
BibRef
Zhang, C.L.[Chen-Lin],
Wu, J.X.[Jian-Xin],
Improving CNN linear layers with power mean non-linearity,
PR(89), 2019, pp. 12-21.
Elsevier DOI
1902
Non-linearity in deep learning, Pre-trained CNN models,
Object recognition, Transfer learning
BibRef
Cruz, L.[Leonel],
Tous, R.[Ruben],
Otero, B.[Beatriz],
Distributed training of deep neural networks with spark:
The MareNostrum experience,
PRL(125), 2019, pp. 174-178.
Elsevier DOI
1909
On parallel system.
Deep Learning, Spark, DL4J, HPC, Performance, Scalability, MareNostrum
BibRef
Sourati, J.[Jamshid],
Gholipour, A.[Ali],
Dy, J.G.[Jennifer G.],
Tomas-Fernandez, X.[Xavier],
Kurugol, S.[Sila],
Warfield, S.K.[Simon K.],
Intelligent Labeling Based on Fisher Information for Medical Image
Segmentation Using Deep Learning,
MedImg(38), No. 11, November 2019, pp. 2642-2653.
IEEE DOI
1911
Training issues.
Image segmentation, Uncertainty, Data models, Biomedical imaging,
Computational modeling, Labeling, Brain modeling,
patch-wise segmentation
BibRef
Liang, J.X.[Jin-Xiu],
Xu, Y.[Yong],
Bao, C.L.[Cheng-Long],
Quan, Y.H.[Yu-Hui],
Ji, H.[Hui],
Barzilai-Borwein-based adaptive learning rate for deep learning,
PRL(128), 2019, pp. 197-203.
Elsevier DOI
1912
Barzilai-Borwein method, Deep neural network,
Stochastic gradient descent, Adaptive learning rate
BibRef
Chun, I.Y.,
Fessler, J.A.,
Convolutional Analysis Operator Learning:
Acceleration and Convergence,
IP(29), 2020, pp. 2108-2122.
IEEE DOI
2001
Convolution, Training, Kernel, Convolutional codes,
Computed tomography, Convergence, Image reconstruction,
X-ray computed tomography
BibRef
Tu, S.S.[Shan-Shan],
ur Rehman, S.[Sadaqat],
Waqas, M.[Muhammad],
ur Rehman, O.[Obaid],
Yang, Z.L.[Zhong-Liang],
Ahmad, B.[Basharat],
Halim, Z.[Zahid],
Zhao, W.[Wei],
Optimisation-based training of evolutionary convolution neural network
for visual classification applications,
IET-CV(14), No. 5, August 2020, pp. 259-267.
DOI Link
2007
BibRef
Lu, Z.,
Deb, K.,
Naresh Boddeti, V.,
MUXConv: Information Multiplexing in Convolutional Neural Networks,
CVPR20(12041-12050)
IEEE DOI
2008
Multiplexing, Computational modeling, Standards,
Predictive models, Convolutional codes, Computational complexity
BibRef
Schult, J.[Jonas],
Engelmann, F.[Francis],
Kontogianni, T.[Theodora],
Leibe, B.[Bastian],
DualConvMesh-Net:
Joint Geodesic and Euclidean Convolutions on 3D Meshes,
CVPR20(8609-8619)
IEEE DOI
2008
Convolutional codes, Kernel, Shape,
Measurement, Convolution, Semantics
BibRef
Liu, Y.S.[Yi-Shu],
Han, Z.Z.[Zheng-Zhuo],
Chen, C.H.[Cong-Hui],
Ding, L.W.[Li-Wang],
Liu, Y.B.[Ying-Bin],
Eagle-Eyed Multitask CNNs for Aerial Image Retrieval and Scene
Classification,
GeoRS(58), No. 9, September 2020, pp. 6699-6721.
IEEE DOI
2008
Image retrieval, Computational modeling, Uncertainty, Training,
Feature extraction, Task analysis, Convolutional neural networks,
similarity distribution learning
BibRef
Liang, C.,
Zhang, H.,
Yuan, D.,
Zhang, M.,
A Novel CNN Training Framework: Loss Transferring,
CirSysVideo(30), No. 12, December 2020, pp. 4611-4625.
IEEE DOI
2012
Training, Computational modeling, Convolutional neural networks,
Benchmark testing, Task analysis, Loss measurement, softmax
BibRef
Zunino, A.[Andrea],
Bargal, S.A.[Sarah Adel],
Morerio, P.[Pietro],
Zhang, J.M.[Jian-Ming],
Sclaroff, S.[Stan],
Murino, V.[Vittorio],
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks,
IJCV(129), No. 4, April 2021, pp. 1139-1152.
Springer DOI
2104
BibRef
Morerio, P.[Pietro],
Cavazza, J.[Jacopo],
Volpi, R.[Riccardo],
Vidal, R.[René],
Murino, V.[Vittorio],
Curriculum Dropout,
ICCV17(3564-3572)
IEEE DOI
1802
Remove NN units to reduce over-specific detectors.
feature extraction, generalisation (artificial intelligence),
image classification, image representation,
Training
BibRef
Dang, Z.[Zheng],
Yi, K.M.[Kwang Moo],
Hu, Y.L.[Yin-Lin],
Wang, F.[Fei],
Fua, P.[Pascal],
Salzmann, M.[Mathieu],
Eigendecomposition-Free Training of Deep Networks for Linear
Least-Square Problems,
PAMI(43), No. 9, September 2021, pp. 3167-3182.
IEEE DOI
2108
BibRef
Earlier:
Eigendecomposition-Free Training of Deep Networks with Zero
Eigenvalue-Based Losses,
ECCV18(VI: 792-807).
Springer DOI
1810
Eigenvalues and eigenfunctions, Machine learning, Optimization, Task analysis,
geometric vision
BibRef
Yuan, G.[Geng],
Chang, S.E.[Sung-En],
Jin, Q.[Qing],
Lu, A.[Alec],
Li, Y.[Yanyu],
Wu, Y.S.[Yu-Shu],
Kong, Z.[Zhenglun],
Xie, Y.[Yanyue],
Dong, P.[Peiyan],
Qin, M.H.[Ming-Hai],
Ma, X.L.[Xiao-Long],
Tang, X.L.[Xu-Long],
Fang, Z.M.[Zhen-Man],
Wang, Y.Z.[Yan-Zhi],
You Already Have It: A Generator-Free Low-Precision DNN Training
Framework Using Stochastic Rounding,
ECCV22(XII:34-51).
Springer DOI
2211
BibRef
Huang, T.[Tao],
You, S.[Shan],
Zhang, B.[Bohan],
Du, Y.X.[Yu-Xuan],
Wang, F.[Fei],
Qian, C.[Chen],
Xu, C.[Chang],
DyRep: Bootstrapping Training with Dynamic Re-parameterization,
CVPR22(578-587)
IEEE DOI
2210
Training, Runtime, Costs, Codes, Computational modeling,
retrieval
BibRef
Tang, Z.D.[Ze-Dong],
Jiang, F.L.[Fen-Long],
Gong, M.[Maoguo],
Li, H.[Hao],
Wu, Y.[Yue],
Yu, F.[Fan],
Wang, Z.D.[Zi-Dong],
Wang, M.[Min],
SKFAC: Training Neural Networks with Faster Kronecker-Factored
Approximate Curvature,
CVPR21(13474-13482)
IEEE DOI
2111
Training, Deep learning, Dimensionality reduction, Neural networks,
Text categorization, Approximation algorithms, Robustness
BibRef
Arenas, R.T.,
Delmas, P.J.,
Strozzi, A.G.,
Development of a Virtual Environment Based Image Generation Tool for
Neural Network Training,
IVCNZ20(1-6)
IEEE DOI
2012
Training, Visualization, Image recognition, Neural networks,
Virtual environments, Tools,
BibRef
Cao, Z.,
Zhang, K.,
Wu, J.,
FPB: Improving Multi-Scale Feature Representation Inside
Convolutional Layer Via Feature Pyramid Block,
ICIP20(1666-1670)
IEEE DOI
2011
Convolution, Lesions, Task analysis, Training, Biomedical imaging,
Biological system modeling, Diseases, multi-scale features,
feature pyramid block
BibRef
Frerix, T.[Thomas],
Nießner, M.[Matthias],
Cremers, D.[Daniel],
Homogeneous Linear Inequality Constraints for Neural Network
Activations,
DeepVision20(3229-3234)
IEEE DOI
2008
Training, Neural networks, Computational modeling, Task analysis,
Optimization, Machine learning, Computer architecture
BibRef
Semih Kayhan, O.,
van Gemert, J.C.,
On Translation Invariance in CNNs: Convolutional Layers Can Exploit
Absolute Spatial Location,
CVPR20(14262-14273)
IEEE DOI
2008
Convolution, Visualization, Machine learning,
Standards, Kernel, Training
BibRef
Zhou, Y.Z.[Yi-Zhou],
Sun, X.Y.[Xiao-Yan],
Luo, C.[Chong],
Zha, Z.J.[Zheng-Jun],
Zeng, W.J.[Wen-Jun],
Spatiotemporal Fusion in 3D CNNs: A Probabilistic View,
CVPR20(9826-9835)
IEEE DOI
2008
CNNs for video.
Spatiotemporal phenomena, Training, Convolutional codes, Kernel
BibRef
Kim, I.[Ildoo],
Baek, W.[Woonhyuk],
Kim, S.[Sungwoong],
Spatially Attentive Output Layer for Image Classification,
CVPR20(9530-9539)
IEEE DOI
2008
Task analysis, Convolution, Aggregates,
Training, Semantics
BibRef
Wang, J.,
Chen, Y.,
Chakraborty, R.,
Yu, S.X.,
Orthogonal Convolutional Neural Networks,
CVPR20(11502-11512)
IEEE DOI
2008
Kernel, Training, Convolutional codes, Redundancy, Task analysis,
Matrix converters, Convolutional neural networks
BibRef
Zhang, X.,
Liu, S.,
Zhang, R.,
Liu, C.,
Huang, D.,
Zhou, S.,
Guo, J.,
Guo, Q.,
Du, Z.,
Zhi, T.,
Chen, Y.,
Fixed-Point Back-Propagation Training,
CVPR20(2327-2335)
IEEE DOI
2008
Training, Quantization (signal), Neural networks, Convergence,
Machine learning, Network architecture, Convolution
BibRef
Wu, C.Y.[Chao-Yuan],
Girshick, R.[Ross],
He, K.M.[Kai-Ming],
Feichtenhofer, C.[Christoph],
Krähenbühl, P.[Philipp],
A Multigrid Method for Efficiently Training Video Models,
CVPR20(150-159)
IEEE DOI
2008
Training, Shape, Computational modeling, Multigrid methods,
Schedules, Biological system modeling, Numerical models
BibRef
Benbihi, A.,
Geist, M.,
Pradalier, C.,
ELF: Embedded Localisation of Features in Pre-Trained CNN,
ICCV19(7939-7948)
IEEE DOI
2004
convolutional neural nets, feature extraction, image matching,
SLAM (robots), ELF, embedded localisation of features, CNN,
BibRef
Cai, Q.[Qi],
Pan, Y.W.[Ying-Wei],
Ngo, C.W.[Chong-Wah],
Tian, X.[Xinmei],
Duan, L.Y.[Ling-Yu],
Yao, T.[Ting],
Exploring Object Relation in Mean Teacher for Cross-Domain Detection,
CVPR19(11449-11458).
IEEE DOI
2002
Using synthetic (rendered) data to train.
BibRef
Ding, R.Z.[Rui-Zhou],
Chin, T.W.[Ting-Wu],
Liu, Z.Y.[Ze-Ye],
Marculescu, D.[Diana],
Regularizing Activation Distribution for Training Binarized Deep
Networks,
CVPR19(11400-11409).
IEEE DOI
2002
BibRef
Zou, F.Y.[Fang-Yu],
Shen, L.[Li],
Jie, Z.Q.[Ze-Qun],
Zhang, W.Z.[Wei-Zhong],
Liu, W.[Wei],
A Sufficient Condition for Convergences of Adam and RMSProp,
CVPR19(11119-11127).
IEEE DOI
2002
adaptive stochastic algorithms for training deep neural networks.
BibRef
Cheng, H.[Hao],
Lian, D.Z.[Dong-Ze],
Deng, B.[Bowen],
Gao, S.H.[Sheng-Hua],
Tan, T.[Tao],
Geng, Y.L.[Yan-Lin],
Local to Global Learning: Gradually Adding Classes for Training Deep
Neural Networks,
CVPR19(4743-4751).
IEEE DOI
2002
BibRef
Laermann, J.[Jan],
Samek, W.[Wojciech],
Strodthoff, N.[Nils],
Achieving Generalizable Robustness of Deep Neural Networks by Stability
Training,
GCPR19(360-373).
Springer DOI
1911
BibRef
Zhang, Z.W.[Zheng-Wen],
Yang, J.[Jian],
Zhang, Z.L.[Zi-Lin],
Li, Y.[Yan],
Cross-Training Deep Neural Networks for Learning from Label Noise,
ICIP19(4100-4104)
IEEE DOI
1910
Deal with label corruption.
Cross-training, label noise, curriculum learning,
deep neural networks, robustness
BibRef
Kamilaris, A.[Andreas],
van den Brink, C.[Corjan],
Karatsiolis, S.[Savvas],
Training Deep Learning Models via Synthetic Data:
Application in Unmanned Aerial Vehicles,
CAIPWS19(81-90).
Springer DOI
1909
BibRef
Cui, L.X.[Li-Xin],
Bai, L.[Lu],
Rossi, L.[Luca],
Wang, Y.[Yue],
Jiao, Y.H.[Yu-Hang],
Hancock, E.R.[Edwin R.],
A Deep Hybrid Graph Kernel Through Deep Learning Networks,
ICPR18(1030-1035)
IEEE DOI
1812
Kernel, Decoding, Training, Tools, Convolution, Reliability
BibRef
Ma, J.B.[Jia-Bin],
Guo, W.Y.[Wei-Yu],
Wang, W.[Wei],
Wang, L.[Liang],
RotateConv: Making Asymmetric Convolutional Kernels Rotatable,
ICPR18(55-60)
IEEE DOI
1812
Kernel, Shape, Convolution, Training, Visualization,
Computational modeling, Image coding
BibRef
An, W.P.[Wang-Peng],
Wang, H.Q.[Hao-Qian],
Sun, Q.Y.[Qing-Yun],
Xu, J.[Jun],
Dai, Q.H.[Qiong-Hai],
Zhang, L.[Lei],
A PID Controller Approach for Stochastic Optimization of Deep
Networks,
CVPR18(8522-8531)
IEEE DOI
1812
Optimization, Training, Acceleration, PD control, PI control, Neural networks
BibRef
Mostajabi, M.[Mohammadreza],
Maire, M.[Michael],
Shakhnarovich, G.[Gregory],
Regularizing Deep Networks by Modeling and Predicting Label Structure,
CVPR18(5629-5638)
IEEE DOI
1812
Training, Task analysis, Decoding, Semantics, Image segmentation,
Cats, Convolutional neural networks
BibRef
Xie, S.Q.[Shu-Qin],
Chen, Z.I.[Zit-Ian],
Xu, C.[Chao],
Lu, C.W.[Ce-Wu],
Environment Upgrade Reinforcement Learning for Non-differentiable
Multi-stage Pipelines,
CVPR18(3810-3819)
IEEE DOI
1812
Training time and complexity.
Training, Pose estimation, Task analysis, Pipelines, Feeds, Object detection
BibRef
Chang, X.B.[Xiao-Bin],
Xiang, T.[Tao],
Hospedales, T.M.[Timothy M.],
Scalable and Effective Deep CCA via Soft Decorrelation,
CVPR18(1488-1497)
IEEE DOI
1812
Canonical Correlation Analysis.
Decorrelation, Computational modeling, Correlation, Training,
Stochastic processes, Optimization, Covariance matrices
BibRef
Hara, K.,
Kataoka, H.,
Satoh, Y.,
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and
ImageNet?,
CVPR18(6546-6555)
IEEE DOI
1812
Kinetic theory, Training, Task analysis, Kernel
BibRef
Yang, Y.,
Zhong, Z.,
Shen, T.,
Lin, Z.,
Convolutional Neural Networks with Alternately Updated Clique,
CVPR18(2413-2422)
IEEE DOI
1812
Training, Convolutional neural networks, Network architecture,
Visualization, Computational modeling, Recurrent neural networks, Neurons
BibRef
Gordon, A.,
Eban, E.,
Nachum, O.,
Chen, B.,
Wu, H.,
Yang, T.,
Choi, E.,
MorphNet: Fast & Simple Resource-Constrained Structure Learning of
Deep Networks,
CVPR18(1586-1595)
IEEE DOI
1812
Biological neural networks, Training,
Computational modeling, Network architecture, Neurons
BibRef
Huangi, L.[Lei],
Huangi, L.[Lei],
Yang, D.W.[Da-Wei],
Lang, B.[Bo],
Deng, J.[Jia],
Decorrelated Batch Normalization,
CVPR18(791-800)
IEEE DOI
1812
Training, Decorrelation, Neural networks,
Principal component analysis, Covariance matrices,
Matrix decomposition
BibRef
Chen, Y.P.[Yun-Peng],
Kalantidis, Y.[Yannis],
Li, J.S.[Jian-Shu],
Yan, S.C.[Shui-Cheng],
Feng, J.S.[Jia-Shi],
Multi-fiber Networks for Video Recognition,
ECCV18(I: 364-380).
Springer DOI
1810
Training with video data.
BibRef
Mopuri, K.R.[Konda Reddy],
Uppala, P.K.[Phani Krishna],
Babu, R.V.[R. Venkatesh],
Ask, Acquire, and Attack:
Data-Free UAP Generation Using Class Impressions,
ECCV18(IX: 20-35).
Springer DOI
1810
Generate appropriate noise for deep training.
BibRef
Jenni, S.[Simon],
Favaro, P.[Paolo],
Deep Bilevel Learning,
ECCV18(X: 632-648).
Springer DOI
1810
Cross-validation to improve training.
BibRef
Rayar, F.[Frédéric],
Uchida, S.[Seiichi],
On Fast Sample Preselection for Speeding up Convolutional Neural
Network Training,
SSSPR18(65-75).
Springer DOI
1810
BibRef
Jiang, C.,
Su, J.,
Gabor Binary Layer in Convolutional Neural Networks,
ICIP18(3408-3412)
IEEE DOI
1809
Training, Feature extraction, Convolutional codes,
Image recognition, Shape, Convolutional neural networks,
image recognition
BibRef
Gillot, P.,
Benois-Pineau, J.,
Zemmari, A.,
Nesterov, Y.,
Increasing Training Stability for Deep CNNS,
ICIP18(3423-3427)
IEEE DOI
1809
Training, Biological neural networks, Optimization, Neurons,
Linear programming, Machine learning, Stochastic processes,
gradient descent
BibRef
Pal, A.,
Arora, C.,
Making Deep Neural Network Fooling Practical,
ICIP18(3428-3432)
IEEE DOI
1809
Robustness, Perturbation methods, Image edge detection,
Neural networks, Training, Distortion, Image generation,
Robustness of Adversarial Attacks
BibRef
Mancini, M.,
Bulò, S.R.,
Caputo, B.,
Ricci, E.,
Best Sources Forward:
Domain Generalization through Source-Specific Nets,
ICIP18(1353-1357)
IEEE DOI
1809
Training, Computational modeling,
Visualization, Semantics, Benchmark testing, Machine learning,
Deep Learning
BibRef
Li, J.,
Dai, T.,
Tang, Q.,
Xing, Y.,
Xia, S.,
Cyclic Annealing Training Convolutional Neural Networks for Image
Classification with Noisy Labels,
ICIP18(21-25)
IEEE DOI
1809
Training, Noise measurement, Cats, Annealing, Robustness, Bagging,
Adaptation models, Image Classification, Noisy Labels,
Bagging CNNs
BibRef
An, W.,
Wang, H.,
Zhang, Y.,
Dai, Q.,
Exponential decay sine wave learning rate for fast deep neural
network training,
VCIP17(1-4)
IEEE DOI
1804
gradient methods, image classification,
learning (artificial intelligence), neural nets, optimisation,
optimization
BibRef
Gupta, K.[Kavya],
Majumdar, A.[Angshul],
Learning autoencoders with low-rank weights,
ICIP17(3899-3903)
IEEE DOI
1803
Artificial neural networks, Biological neural networks, Decoding,
Neurons, Noise reduction, Redundancy, Training, autoencoder,
nuclear norm
BibRef
Chadha, A.,
Abbas, A.,
Andreopoulos, Y.,
Compressed-domain video classification with deep neural networks:
'There's way too much information to decode the matrix',
ICIP17(1832-1836)
IEEE DOI
1803
Neural networks, Optical imaging, Optical network units, Standards,
Training, classification,
video coding
BibRef
Bochinski, E.,
Senst, T.,
Sikora, T.,
Hyper-parameter optimization for convolutional neural network
committees based on evolutionary algorithms,
ICIP17(3924-3928)
IEEE DOI
1803
Error analysis, Evolutionary computation, Kernel, Optimization,
Sociology, Statistics, Training, Convolutional Neural Network,
MNIST
BibRef
Dahia, G.,
Santos, M.,
Segundo, M.P.,
A study of CNN outside of training conditions,
ICIP17(3820-3824)
IEEE DOI
1803
Color, Databases, Face, Face recognition, Image color analysis,
Machine learning, Training, CNNs, Deep Learning, Face Recognition
BibRef
Zhong, Y.,
Ettinger, G.,
Enlightening Deep Neural Networks with Knowledge of Confounding
Factors,
CEFR-LCV17(1077-1086)
IEEE DOI
1802
Biological neural networks, Data models, Neurons,
Object recognition, Training
BibRef
Kolkin, N.,
Shakhnarovich, G.,
Shechtman, E.,
Training Deep Networks to be Spatially Sensitive,
ICCV17(5669-5678)
IEEE DOI
1802
Spatial issues.
approximation theory, computational complexity,
gradient methods, image denoising, image segmentation,
Training
BibRef
Yu, A.[Aron],
Grauman, K.[Kristen],
Semantic Jitter:
Dense Supervision for Visual Comparisons via Synthetic Images,
ICCV17(5571-5580)
IEEE DOI
1802
Augment real training images by artivicial noisy images.
image processing, learning (artificial intelligence),
dense supervision, fashion images, semantic jitter,
Visualization
BibRef
Zhang, F.H.[Fei-Hu],
Wah, B.W.[Benjamin W.],
Supplementary Meta-Learning:
Towards a Dynamic Model for Deep Neural Networks,
ICCV17(4354-4363)
IEEE DOI
1802
Network results depend on image.
image classification, image resolution,
learning (artificial intelligence), neural nets, MLNN, SNN,
Training
BibRef
Kamiya, R.,
Yamashita, T.,
Ambai, M.,
Sato, I.,
Yamauchi, Y.,
Fujiyoshi, H.,
Binary-Decomposed DCNN for Accelerating Computation and Compressing
Model Without Retraining,
CEFR-LCV17(1095-1102)
IEEE DOI
1802
Acceleration, Approximation algorithms, Computational modeling,
Image recognition, Matrix decomposition, Quantization (signal)
BibRef
Li, Y.H.[Yang-Hao],
Wang, N.Y.[Nai-Yan],
Liu, J.Y.[Jia-Ying],
Hou, X.D.[Xiao-Di],
Factorized Bilinear Models for Image Recognition,
ICCV17(2098-2106)
IEEE DOI
1802
added layer to CNN.
convolution, image recognition, image representation,
learning (artificial intelligence), matrix decomposition,
Training
BibRef
Xie, D.,
Xiong, J.,
Pu, S.,
All You Need is Beyond a Good Init: Exploring Better Solution for
Training Extremely Deep Convolutional Neural Networks with
Orthonormality and Modulation,
CVPR17(5075-5084)
IEEE DOI
1711
Convolution, Jacobian matrices, Modulation,
Network architecture, Neural networks, Training
BibRef
Chen, B.H.[Bing-Hui],
Deng, W.H.[Wei-Hong],
Du, J.P.[Jun-Ping],
Noisy Softmax: Improving the Generalization Ability of DCNN via
Postponing the Early Softmax Saturation,
CVPR17(4021-4030)
IEEE DOI
1711
Annealing, Noise measurement, Robustness, Standards,
Telecommunications, Training
BibRef
Patrini, G.[Giorgio],
Rozza, A.[Alessandro],
Menon, A.K.[Aditya Krishna],
Nock, R.[Richard],
Qu, L.Z.[Li-Zhen],
Making Deep Neural Networks Robust to Label Noise:
A Loss Correction Approach,
CVPR17(2233-2241)
IEEE DOI
1711
Clothing, Neural networks,
Noise measurement, Robustness, Training
BibRef
Kokkinos, I.,
UberNet: Training a Universal Convolutional Neural Network for Low-,
Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory,
CVPR17(5454-5463)
IEEE DOI
1711
Discrete wavelet transforms, Estimation,
Proposals, Semantics, Training
BibRef
Bagherinezhad, H.,
Rastegari, M.,
Farhadi, A.,
LCNN: Lookup-Based Convolutional Neural Network,
CVPR17(860-869)
IEEE DOI
1711
Computational modeling, Dictionaries, Machine learning,
Neural networks, Solid modeling, Tensile stress, Training
BibRef
Juefei-Xu, F.[Felix],
Boddeti, V.N.,
Savvides, M.[Marios],
Local Binary Convolutional Neural Networks,
CVPR17(4284-4293)
IEEE DOI
1711
Computational modeling, Convolution, Encoding,
Neural networks, Standards, Training
BibRef
Liu, X.,
Li, S.,
Kan, M.,
Shan, S.,
Chen, X.,
Self-Error-Correcting Convolutional Neural Network for Learning with
Noisy Labels,
FG17(111-117)
IEEE DOI
1707
Biological neural networks, Face, Neurons, Noise measurement,
Noise robustness, Switches, Training
BibRef
Xu, X.,
Todorovic, S.,
Beam search for learning a deep Convolutional Neural Network of 3D
shapes,
ICPR16(3506-3511)
IEEE DOI
1705
Computational modeling,
Knowledge transfer, Shape, Solid modeling, Training
BibRef
Gwon, Y.[Youngjune],
Cha, M.[Miriam],
Kung, H.T.,
Deep Sparse-coded Network (DSN),
ICPR16(2610-2615)
IEEE DOI
1705
Backpropagation, Dictionaries, Encoding,
Neural networks, Nonhomogeneous media, Training
BibRef
Teerapittayanon, S.,
McDanel, B.,
Kung, H.T.,
BranchyNet:
Fast inference via early exiting from deep neural networks,
ICPR16(2464-2469)
IEEE DOI
1705
Entropy, Feedforward neural networks, Inference algorithms,
Optimization, Runtime, Training
BibRef
Pham, T.,
Tran, T.,
Phung, D.,
Venkatesh, S.,
Faster training of very deep networks via p-norm gates,
ICPR16(3542-3547)
IEEE DOI
1705
Feedforward neural networks, Logic gates,
Road transportation, Standards, Training
BibRef
Kabkab, M.,
Hand, E.,
Chellappa, R.,
On the size of Convolutional Neural Networks and generalization
performance,
ICPR16(3572-3577)
IEEE DOI
1705
Boolean functions, Databases, Feedforward neural networks,
Probability distribution, Testing, Training
BibRef
Uchida, K.,
Tanaka, M.,
Okutomi, M.,
Coupled convolution layer for convolutional neural network,
ICPR16(3548-3553)
IEEE DOI
1705
Cells (biology), Convolution, Optical imaging, Photonics,
Photoreceptors, Retina, Training
BibRef
Wang, Y.Q.[Ye-Qing],
Li, Y.[Yi],
Porikli, F.M.[Fatih M.],
Finetuning Convolutional Neural Networks for visual aesthetics,
ICPR16(3554-3559)
IEEE DOI
1705
Feature extraction, Machine learning,
Neural networks, Semantics, Training, Visualization, Deep learning,
visual, aesthetics
BibRef
Tobías, L.,
Ducournau, A.,
Rousseau, F.,
Mercier, G.,
Fablet, R.,
Convolutional Neural Networks for object recognition on mobile
devices: A case study,
ICPR16(3530-3535)
IEEE DOI
1705
Biological neural networks, Computational modeling,
Feature extraction, Kernel, Mobile handsets,
Training, Convolutional Neural Networks, Deep Learning,
Machine Learning, Mobile Devices, Object, Detection
BibRef
Afridi, M.J.,
Ross, A.,
Shapiro, E.M.,
L-CNN: Exploiting labeling latency in a CNN learning framework,
ICPR16(2156-2161)
IEEE DOI
1705
Biomedical imaging, Labeling,
Magnetic resonance imaging, Microprocessors, Testing, Training
BibRef
Ghaderi, A.,
Athitsos, V.,
Selective unsupervised feature learning with Convolutional Neural
Network (S-CNN),
ICPR16(2486-2490)
IEEE DOI
1705
Classification algorithms, Convolutional codes, Kernel,
Neural networks, Search problems, Support vector machines,
Training, Artificial Neural Networks,
Classification and Clustring, Deep, Learning
BibRef
Kirillov, A.,
Schlesinger, D.,
Zheng, S.,
Savchynskyy, B.,
Torr, P.H.S.[Philip H.S.],
Rother, C.,
Joint Training of Generic CNN-CRF Models with Stochastic Optimization,
ACCV16(II: 221-236).
Springer DOI
1704
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Pooling in Convolutional Neural Networks Implementations .