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Information Dropout: Learning Optimal Representations Through Noisy
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PAMI(40), No. 12, December 2018, pp. 2897-2905.
IEEE DOI
1811
Neural networks, Deep learning, Bayes methods, Machine learning,
Information theory, Noise measurement, Learning systems,
minimality
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DECODE: Deep Confidence Network for Robust Image Classification,
IP(28), No. 8, August 2019, pp. 3752-3765.
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convolutional neural nets, data visualisation,
image classification, image denoising, confidence model
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IEEE DOI
2008
Training, Task analysis, Robustness, Cost function, Neurons,
Neural networks, Deep learning, information bottleneck,
stochastic neural networks
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On the expected behaviour of noise regularised deep neural networks
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Elsevier DOI
2010
Neural networks, Gaussian processes, Signal propagation, Noise regularisation
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Elsevier DOI
2110
Image classification, Deep neural networks, Weak supervisor
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Adversarial Margin Maximization Networks,
PAMI(43), No. 4, April 2021, pp. 1129-1139.
IEEE DOI
2103
Perturbation methods, Training, Support vector machines,
Distortion, Radio frequency, Robustness, Neural networks,
deep neural networks
BibRef
Li, S.[Shuai],
Jia, K.[Kui],
Wen, Y.X.[Yu-Xin],
Liu, T.L.[Tong-Liang],
Tao, D.C.[Da-Cheng],
Orthogonal Deep Neural Networks,
PAMI(43), No. 4, April 2021, pp. 1352-1368.
IEEE DOI
2103
Training, Robustness, Jacobian matrices, Task analysis,
Neural networks, Optimization, Deep learning, Deep neural networks,
image classification
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Kortylewski, A.[Adam],
Liu, Q.[Qing],
Wang, A.T.[Ang-Tian],
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A Robust and Interpretable Model for Object Recognition Under Occlusion,
IJCV(129), No. 3, March 2021, pp. 736-760.
Springer DOI
2103
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Kortylewski, A.,
He, J.,
Liu, Q.,
Yuille, A.L.,
Compositional Convolutional Neural Networks:
A Deep Architecture With Innate Robustness to Partial Occlusion,
CVPR20(8937-8946)
IEEE DOI
2008
Robustness, Training, Computational modeling, Solid modeling,
Artificial neural networks
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Wang, Q.L.[Qi-Long],
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Deep CNNs Meet Global Covariance Pooling:
Better Representation and Generalization,
PAMI(43), No. 8, August 2021, pp. 2582-2597.
IEEE DOI
2107
Covariance matrices, Robustness, Estimation, Geometry, Measurement,
Visualization, Complexity theory, Global covariance pooling,
visual recognition
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Genzel, M.[Martin],
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März, M.[Maximilian],
Solving Inverse Problems With Deep Neural Networks:
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PAMI(45), No. 1, January 2023, pp. 1119-1134.
IEEE DOI
2212
Artificial neural networks, Image reconstruction, Robustness,
Inverse problems, Deep learning, Perturbation methods, medical imaging
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Yu, C.[Cheng],
Chen, J.S.[Jian-Sheng],
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Zhong, Y.[Yuanyi],
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Shaping Deep Feature Space Towards Gaussian Mixture for Visual
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PAMI(45), No. 2, February 2023, pp. 2430-2444.
IEEE DOI
2301
Task analysis, Face recognition, Visualization, Training, Robustness,
Shape, Neural networks, Convolutional neural networks, adversarial robustness
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Shu, J.[Jun],
Yuan, X.[Xiang],
Meng, D.Y.[De-Yu],
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CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust
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2310
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Chen, D.D.[Dong-Dong],
Tachella, J.[Julián],
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Robust Equivariant Imaging: A fully unsupervised framework for
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CVPR22(5637-5646)
IEEE DOI
2210
WWW Link. With noise.
Training, Photography, Inverse problems, Computational modeling,
Imaging, Self-supervised learning, Performance gain,
Self- semi- meta- unsupervised learning
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Valmadre, J.[Jack],
Arnab, A.[Anurag],
Schmid, C.[Cordelia],
Learning with Neighbor Consistency for Noisy Labels,
CVPR22(4662-4671)
IEEE DOI
2210
Training, Deep learning, Stochastic processes,
Semisupervised learning,
Deep learning architectures and techniques
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Zhu, L.C.[Lin-Chao],
Jiang, L.[Lu],
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Faster Meta Update Strategy for Noise-Robust Deep Learning,
CVPR21(144-153)
IEEE DOI
2111
Training, Deep learning, Training data, Robustness,
Pattern recognition, Noise robustness
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Li, Y.[Yao],
Min, M.R.[Martin Renqiang],
Lee, T.[Thomas],
Yu, W.C.[Wen-Chao],
Kruus, E.[Erik],
Wang, W.[Wei],
Hsieh, C.J.[Cho-Jui],
Towards Robustness of Deep Neural Networks via Regularization,
ICCV21(7476-7485)
IEEE DOI
2203
Deep learning, Manifolds, Analytical models,
Computational modeling, Neural networks, Benchmark testing,
Recognition and classification
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Wang, X.S.[Xin-Shao],
Hua, Y.[Yang],
Kodirov, E.[Elyor],
Clifton, D.A.[David A.],
Robertson, N.M.[Neil M.],
ProSelfLC:
Progressive Self Label Correction for Training Robust Deep Neural Networks,
CVPR21(752-761)
IEEE DOI
2111
Training, Deep learning, Semantics, Neural networks,
Predictive models, Minimization, Entropy
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Dong, X.Y.[Xiao-Yi],
Chen, D.D.[Dong-Dong],
Zhou, H.[Hang],
Hua, G.[Gang],
Zhang, W.M.[Wei-Ming],
Yu, N.H.[Neng-Hai],
Self-Robust 3D Point Recognition via Gather-Vector Guidance,
CVPR20(11513-11521)
IEEE DOI
2008
Robustness, Perturbation methods,
Training, Image restoration, Aircraft
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Qian, Q.[Qi],
Hu, J.H.[Ju-Hua],
Li, H.[Hao],
Hierarchically Robust Representation Learning,
CVPR20(7334-7342)
IEEE DOI
2008
Robustness, Task analysis, Feature extraction, Optimization,
Benchmark testing, Training, Data models
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Rodríguez-Rodríguez, J.A.[José A.],
Molina-Cabello, M.A.[Miguel A.],
Benítez-Rochel, R.[Rafaela],
López-Rubio, E.[Ezequiel],
The Effect of Noise and Brightness on Convolutional Deep Neural
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MOI2QDN20(639-654).
Springer DOI
2103
BibRef
Zhang, R.,
Peng, Z.,
Wu, L.,
Li, Z.,
Luo, P.,
Exemplar Normalization for Learning Deep Representation,
CVPR20(12723-12732)
IEEE DOI
2008
Task analysis, Training, Tensile stress, Standards, Switches,
Noise measurement, Benchmark testing
BibRef
Jaiswal, M.S.,
Kang, B.,
Lee, J.,
Cho, M.,
MUTE: Inter-class Ambiguity Driven Multi-hot Target Encoding for Deep
Neural Network Design,
DeepVision20(3254-3263)
IEEE DOI
2008
Encoding, Neural networks, Hamming distance, Noise measurement,
Semantics, Computational modeling, Training
BibRef
Huang, Y.,
Yu, Y.,
An Internal Covariate Shift Bounding Algorithm for Deep Neural
Networks by Unitizing Layers' Outputs,
CVPR20(8462-8470)
IEEE DOI
2008
Integrated circuits, Upper bound, Training, Neural networks,
Convergence, Noise measurement, Earth
BibRef
Wang, Z.[Zhen],
Hu, G.S.[Guo-Sheng],
Hu, Q.H.[Qing-Hua],
Training Noise-Robust Deep Neural Networks via Meta-Learning,
CVPR20(4523-4532)
IEEE DOI
2008
Noise measurement, Optimization, Training, Noise robustness,
Natural language processing, Robustness
BibRef
Zhang, L.,
Qi, G.,
WCP: Worst-Case Perturbations for Semi-Supervised Deep Learning,
CVPR20(3911-3920)
IEEE DOI
2008
Perturbation methods, Training, Additives, Robustness,
Predictive models, Data models, Additive noise
BibRef
Han, J.,
Luo, P.,
Wang, X.,
Deep Self-Learning From Noisy Labels,
ICCV19(5137-5146)
IEEE DOI
2004
data handling, learning (artificial intelligence), neural nets,
robust network, noisy labels, clean data,
Optimization
BibRef
Gowal, S.[Sven],
Dvijotham, K.[Krishnamurthy],
Stanforth, R.[Robert],
Bunel, R.[Rudy],
Qin, C.[Chongli],
Uesato, J.[Jonathan],
Arandjelovic, R.[Relja],
Mann, T.A.[Timothy Arthur],
Kohli, P.[Pushmeet],
Scalable Verified Training for Provably Robust Image Classification,
ICCV19(4841-4850)
IEEE DOI
2004
Robustness, Training, Neural networks, Perturbation methods,
Upper bound, Adaptation models, Optimization
BibRef
Wang, Y.S.[Yi-Sen],
Ma, X.J.[Xing-Jun],
Chen, Z.Y.[Zai-Yi],
Luo, Y.[Yuan],
Yi, J.F.[Jin-Feng],
Bailey, J.[James],
Symmetric Cross Entropy for Robust Learning With Noisy Labels,
ICCV19(322-330)
IEEE DOI
2004
entropy, neural nets, symmetric cross entropy learning,
noise robust counterpart reverse cross entropy, noisy labels, Task analysis
BibRef
Nazaré, T.S.[Tiago S.],
da Costa, G.B.P.[Gabriel B. Paranhos],
Contato, W.A.[Welinton A.],
Ponti, M.P.[Moacir P.],
Deep Convolutional Neural Networks and Noisy Images,
CIARP17(416-424).
Springer DOI
1802
BibRef
Rodner, E.[Erik],
Simon, M.[Marcel],
Fisher, R.[Robert],
Denzler, J.[Joachim],
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of
Convolutional Neural Networks Approaches,
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HTML Version.
1805
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Structural Description, Spatial Descriptions in Deep Networks .