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Algorithm design and analysis.
Sample labels are randomly corrupted.
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Noisy labels, KL entropy, Mix-up loss, DNN
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Semi-supervised learning, Classification, Graph-based, Filter, Label noise
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Learning from Noisy Labels via Discrepant Collaborative Training,
WACV20(3158-3167)
IEEE DOI
2006
Co-training, Noisy labels, Weak-supervised learning.
Noise measurement, Training, Collaboration, Task analysis,
Feature extraction, Discrete cosine transforms, Neural networks
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Sun, H.L.[Hao-Liang],
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Elsevier DOI
2203
Label noise, Meta-learning, Probabilistic model, Robust learning
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MetaLabelNet: Learning to Generate Soft-Labels From Noisy-Labels,
IP(31), 2022, pp. 4352-4362.
IEEE DOI
2207
Training, Noise measurement, Noise robustness, Feature extraction,
Training data, Deep learning, Wide band gap semiconductors, meta-learning
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Yi, R.[Rumeng],
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TC-Net: Detecting Noisy Labels Via Transform Consistency,
MultMed(24), 2022, pp. 4328-4341.
IEEE DOI
2210
Noise measurement, Training, Visualization, Transforms,
Predictive models, Perturbation methods, Airplanes, Noisy labels,
visual attention consistency
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Sample Selection Approach with Number of False Predictions for Learning
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IP(31), 2022, pp. 6369-6380.
IEEE DOI
2211
Annotations, Noise measurement, Detectors, Task analysis, Training,
Object detection, Degradation, Object detection, noisy label,
teacher-student learning
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Xu, Z.[Zhe],
Lu, D.[Donghuan],
Luo, J.[Jie],
Wang, Y.X.[Yi-Xin],
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Anti-Interference From Noisy Labels: Mean-Teacher-Assisted Confident
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MedImg(41), No. 11, November 2022, pp. 3062-3073.
IEEE DOI
2211
Image segmentation, Noise measurement, Training,
Biomedical imaging, Task analysis, Quality control,
label denoising
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Zhang, L.[Le],
Tanno, R.[Ryutaro],
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Huang, Y.W.[Ya-Wen],
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Jacob, J.[Joseph],
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Learning from multiple annotators for medical image segmentation,
PR(138), 2023, pp. 109400.
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2303
Multi-Annotator, Label fusion, Segmentation
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On better detecting and leveraging noisy samples for learning with
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Elsevier DOI
2301
Severe label noise, Lipschitz regularization,
Adaptive modeling and detection of label noise, Semi-supervised learning
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Gong, C.[Chen],
Ding, Y.L.[Yong-Liang],
Han, B.[Bo],
Niu, G.[Gang],
Yang, J.[Jian],
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Sugiyama, M.[Masashi],
Class-Wise Denoising for Robust Learning Under Label Noise,
PAMI(45), No. 3, March 2023, pp. 2835-2848.
IEEE DOI
2302
Noise measurement, Training, Entropy, Estimation, Neural networks,
Matrix decomposition, Fasteners, Label noise, centroid estimation,
variance reduction
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Xia, X.B.[Xiao-Bo],
Han, B.[Bo],
Wang, N.N.[Nan-Nan],
Deng, J.K.[Jian-Kang],
Li, J.T.[Jia-Tong],
Mao, Y.[Yinian],
Liu, T.L.[Tong-Liang],
Extended T: Learning With Mixed Closed-Set and Open-Set Noisy Labels,
PAMI(45), No. 3, March 2023, pp. 3047-3058.
IEEE DOI
2302
Noise measurement, Training, Training data, Data models,
Search engines, Face recognition, Computer science, robustness
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Li, S.K.[Shi-Kun],
Liu, T.L.[Tong-Liang],
Tan, J.Y.[Ji-Yong],
Zeng, D.[Dan],
Ge, S.M.[Shi-Ming],
Trustable Co-Label Learning From Multiple Noisy Annotators,
MultMed(25), 2023, pp. 1045-1057.
IEEE DOI
2303
Noise measurement, Data models, Training, Labeling,
Supervised learning, Robustness, Deep learning, Label noise,
learning from crowds
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Liang, C.[Chao],
Yang, Z.X.[Zong-Xin],
Zhu, L.C.[Lin-Chao],
Yang, Y.[Yi],
Co-Learning Meets Stitch-Up for Noisy Multi-Label Visual Recognition,
IP(32), 2023, pp. 2508-2519.
IEEE DOI
2305
Noise measurement, Training, Dogs, Deep learning, Annotations,
Visualization, Training data, Noisy labels,
deep learning
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Gong, X.W.[Xiu-Wen],
Yuan, D.[Dong],
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Luo, F.[Fulin],
A Unifying Probabilistic Framework for Partially Labeled Data
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PAMI(45), No. 7, July 2023, pp. 8036-8048.
IEEE DOI
2306
Phase locked loops, Correlation, Training, Probabilistic logic,
Testing, Task analysis, Noise measurement, classification
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Zhao, Z.L.[Zhi-Lin],
Cao, L.B.[Long-Bing],
Lin, K.Y.[Kun-Yu],
Revealing the Distributional Vulnerability of Discriminators by
Implicit Generators,
PAMI(45), No. 7, July 2023, pp. 8888-8901.
IEEE DOI
2306
Generators, Training, Standards, Entropy, Detectors, Sensitivity,
Neural networks, Deep learning, generator-discriminator,
shannon entropy
BibRef
Yao, J.C.[Jiang-Chao],
Han, B.[Bo],
Zhou, Z.H.[Zhi-Han],
Zhang, Y.[Ya],
Tsang, I.W.[Ivor W.],
Latent Class-Conditional Noise Model,
PAMI(45), No. 8, August 2023, pp. 9964-9980.
IEEE DOI
2307
Noise measurement, Training, Optimization, Deep learning,
Bayes methods, Robustness, Computational modeling,
semi-supervised learning
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Wang, L.[Lin],
Xu, X.M.[Xiang-Min],
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Liu, F.[Fang],
Reflective Learning With Label Noise,
CirSysVideo(33), No. 7, July 2023, pp. 3343-3357.
IEEE DOI
2307
Noise measurement, Training, Iterative methods, Task analysis, Entropy,
Optimization, Transfer learning, Reflective learning, dynamic iterative function
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Chen, Y.Q.[Yuan-Qi],
Jin, C.[Cece],
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Li, T.H.[Thomas H.],
Gao, W.[Wei],
Mitigating Label Noise in GANs via Enhanced Spectral Normalization,
CirSysVideo(33), No. 8, August 2023, pp. 3924-3934.
IEEE DOI
2308
Noise measurement, Training, Task analysis, Image synthesis,
Generators, Generative adversarial networks, Neural networks,
learning with noisy labels
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Sumbul, G.[Gencer],
Demir, B.[Begum],
Generative Reasoning Integrated Label Noise Robust Deep Image
Representation Learning,
IP(32), 2023, pp. 4529-4542.
IEEE DOI
2309
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Gong, Y.S.[Yong-Shun],
Li, Z.B.[Zhi-Bin],
Liu, W.[Wei],
Lu, X.[Xiankai],
Liu, X.W.[Xin-Wang],
Tsang, I.W.[Ivor W.],
Yin, Y.L.[Yi-Long],
Missingness-Pattern-Adaptive Learning With Incomplete Data,
PAMI(45), No. 9, September 2023, pp. 11053-11066.
IEEE DOI
2309
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Liu, D.[Defu],
Li, W.[Wen],
Duan, L.X.[Li-Xin],
Tsang, I.W.[Ivor W.],
Yang, G.W.[Guo-Wu],
Noisy Label Learning With Provable Consistency for a Wider Family of
Losses,
PAMI(45), No. 11, November 2023, pp. 13536-13552.
IEEE DOI
2310
BibRef
Liu, F.C.[Fu-Chang],
Wang, Y.[Yu],
Li, Z.[Zheng],
Pan, Z.G.[Zhi-Geng],
GEIKD: Self-knowledge distillation based on gated ensemble networks
and influences-based label noise removal,
CVIU(235), 2023, pp. 103771.
Elsevier DOI
2310
Self-distillation, Gated ensemble network,
Influences estimation, Noisy labels
BibRef
Zhao, X.M.[Xue-Mei],
Cheng, Y.[Yong],
Liang, L.[Luo],
Wang, H.J.[Hai-Jian],
Gao, X.Y.[Xing-Yu],
Wu, J.[Jun],
A balanced random learning strategy for CNN based Landsat image
segmentation under imbalanced and noisy labels,
PR(144), 2023, pp. 109824.
Elsevier DOI
2310
Landsat image segmentation, Noisy labels, Confidence interval,
Random learning, Multi-layer features
BibRef
Yan, Y.[Yan],
Xu, Y.[Youze],
Xue, J.H.[Jing-Hao],
Lu, Y.[Yang],
Wang, H.Z.[Han-Zi],
Zhu, W.T.[Wen-Tao],
Drop Loss for Person Attribute Recognition With Imbalanced
Noisy-Labeled Samples,
Cyber(53), No. 11, November 2023, pp. 7071-7084.
IEEE DOI
2310
BibRef
Mao, S.[Shunan],
Wang, Y.[Yaowei],
Wang, X.Y.[Xiao-Yu],
Zhang, S.L.[Shi-Liang],
Multi-proxy feature learning for robust fine-grained visual
recognition,
PR(143), 2023, pp. 109779.
Elsevier DOI
2310
Fine-grained visual recognition, Noisy label, Long tail, Proxy learning
BibRef
Mao, S.[Shunan],
Zhang, S.L.[Shi-Liang],
Robust Fine-Grained Visual Recognition With Neighbor-Attention Label
Correction,
IP(33), 2024, pp. 2614-2626.
IEEE DOI
2404
Training, Noise measurement, Visualization, Optimization,
Task analysis, Feature extraction, Annotations, Noisy label,
semantic segmentation
BibRef
Liu, C.Y.[Cheng-Yang],
Zhao, L.[Lin],
Wu, H.B.[Hai-Bin],
Hyperspectral Images Weakly Supervised Classification with Noisy
Labels,
RS(15), No. 20, 2023, pp. 4994.
DOI Link
2310
BibRef
Li, X.C.[Xiu-Chuan],
Xia, X.B.[Xiao-Bo],
Zhu, F.[Fei],
Liu, T.L.[Tong-Liang],
Zhang, X.Y.[Xu-Yao],
Liu, C.L.[Cheng-Lin],
Dynamics-aware loss for learning with label noise,
PR(144), 2023, pp. 109835.
Elsevier DOI
2310
Label noise, Dynamics, Robust loss function
BibRef
Yang, S.[Shuo],
Wu, S.[Songhua],
Yang, E.[Erkun],
Han, B.[Bo],
Liu, Y.[Yang],
Xu, M.[Min],
Niu, G.[Gang],
Liu, T.L.[Tong-Liang],
A Parametrical Model for Instance-Dependent Label Noise,
PAMI(45), No. 12, December 2023, pp. 14055-14068.
IEEE DOI
2311
BibRef
Wan, J.[Jia],
Wu, Q.Q.[Qiang-Qiang],
Chan, A.B.[Antoni B],
Modeling Noisy Annotations for Point-Wise Supervision,
PAMI(45), No. 12, December 2023, pp. 15065-15080.
IEEE DOI
2311
BibRef
Chen, Q.Q.[Qing-Qiang],
Jiang, G.X.[Gao-Xia],
Cao, F.Y.[Fu-Yuan],
Men, C.Q.[Chang-Qian],
Wang, W.J.[Wen-Jian],
A general elevating framework for label noise filters,
PR(147), 2024, pp. 110072.
Elsevier DOI
2312
Classification, Label noise, Noise filter, Sample reduction
BibRef
Feng, Z.[Zerun],
Zeng, Z.M.[Zhi-Min],
Guo, C.[Caili],
Li, Z.[Zheng],
Hu, L.[Lin],
Learning From Noisy Correspondence With Tri-Partition for Cross-Modal
Matching,
MultMed(26), 2024, pp. 3884-3896.
IEEE DOI
2402
Noise measurement, Semantics, Training, Semisupervised learning, Data models,
Costs, Visualization, Cross-modal matching, video-text matching
BibRef
Lacy, F.[Fred],
Ruiz-Reyes, A.[Angel],
Brescia, A.[Anthony],
Machine learning for low signal-to-noise ratio detection,
PRL(179), 2024, pp. 115-122.
Elsevier DOI
2403
Anomaly, artificial intelligence, Long short-term memory,
Low signal-to-noise, Neural network, Signal detection, Surveillance
BibRef
Roizman, V.[Violeta],
Jonckheere, M.[Matthieu],
Pascal, F.[Fr©d©ric],
A Flexible EM-Like Clustering Algorithm for Noisy Data,
PAMI(46), No. 5, May 2024, pp. 2709-2721.
IEEE DOI
2404
Clustering algorithms, Estimation, Covariance matrices,
Data models, Shape, Gaussian distribution, Task analysis, Clustering,
semi-parametric model
BibRef
Xia, X.B.[Xiao-Bo],
Lu, P.Q.[Peng-Qian],
Gong, C.[Chen],
Han, B.[Bo],
Yu, J.[Jun],
Yu, J.[Jun],
Liu, T.L.[Tong-Liang],
Regularly Truncated M-Estimators for Learning With Noisy Labels,
PAMI(46), No. 5, May 2024, pp. 3522-3536.
IEEE DOI
2404
Noise measurement, Training, Training data, Computer science,
Switches, Random variables, Australia, Learning with noisy labels,
generalization
BibRef
Wang, Y.K.[Yi-Kai],
Fu, Y.W.[Yan-Wei],
Sun, X.W.[Xin-Wei],
Knockoffs-SPR: Clean Sample Selection in Learning With Noisy Labels,
PAMI(46), No. 5, May 2024, pp. 3242-3256.
IEEE DOI
2404
Noise measurement, Training, Robustness, Feature extraction,
Degradation, Data models, Sun, Knockoffs method,
type-two error control
BibRef
Zhang, J.F.[Jing-Feng],
Song, B.[Bo],
Wang, H.H.[Hao-Han],
Han, B.[Bo],
Liu, T.L.[Tong-Liang],
Liu, L.[Lei],
Sugiyama, M.[Masashi],
BadLabel: A Robust Perspective on Evaluating and Enhancing
Label-Noise Learning,
PAMI(46), No. 6, June 2024, pp. 4398-4409.
IEEE DOI Code:
WWW Link.
2405
Noise measurement, Training, Task analysis, Standards, Arrays,
Optimization, Data models, Robust label-noise learning,
a challenging type of label noise
BibRef
Zhang, S.[Shuo],
Li, J.Q.[Jian-Qing],
Fujita, H.[Hamido],
Li, Y.W.[Yu-Wen],
Wang, D.B.[Deng-Bao],
Zhu, T.T.[Ting-Ting],
Zhang, M.L.[Min-Ling],
Liu, C.Y.[Cheng-Yu],
Student Loss: Towards the Probability Assumption in Inaccurate
Supervision,
PAMI(46), No. 6, June 2024, pp. 4460-4475.
IEEE DOI
2405
Noise measurement, Training, Task analysis, Robustness, Optimization,
Labeling, Probability distribution, Deep learning, robust loss function
BibRef
Wu, S.[Songhua],
Zhou, T.Y.[Tian-Yi],
Du, Y.X.[Yu-Xuan],
Yu, J.[Jun],
Han, B.[Bo],
Liu, T.L.[Tong-Liang],
A Time-Consistency Curriculum for Learning From Instance-Dependent
Noisy Labels,
PAMI(46), No. 7, July 2024, pp. 4830-4842.
IEEE DOI
2406
Training, Noise measurement, Data models, Computational modeling,
Estimation, Predictive models, Computer science,
image classification
BibRef
Hell, M.[Maximilian],
Brandmeier, M.[Melanie],
Identifying Plausible Labels from Noisy Training Data for a Land Use
and Land Cover Classification Application in Amazônia Legal,
RS(16), No. 12, 2024, pp. 2080.
DOI Link
2406
BibRef
Liu, X.T.[Xiao-Tong],
Wang, J.X.[Jin-Xin],
Wang, D.[Di],
Lin, S.B.[Shao-Bo],
Weighted Spectral Filters for Kernel Interpolation on Spheres:
Estimates of Prediction Accuracy for Noisy Data,
SIIMS(17), No. 2, 2024, pp. 951-983.
DOI Link
2407
BibRef
Li, Z.[Zina],
Yang, X.R.[Xiao-Rui],
Meng, D.Y.[De-Yu],
Cao, X.[Xiangyong],
An Adaptive Noisy Label-Correction Method Based on Selective Loss for
Hyperspectral Image-Classification Problem,
RS(16), No. 13, 2024, pp. 2499.
DOI Link
2407
BibRef
Wang, J.Y.[Jing-Yi],
Xia, X.B.[Xiao-Bo],
Lan, L.[Long],
Wu, X.H.[Xing-Hao],
Yu, J.[Jun],
Yang, W.J.[Wen-Jing],
Han, B.[Bo],
Liu, T.L.[Tong-Liang],
Tackling Noisy Labels With Network Parameter Additive Decomposition,
PAMI(46), No. 9, September 2024, pp. 6341-6354.
IEEE DOI
2408
Noise measurement, Training, Additives, Robustness, Training data,
Noise robustness, Upper bound, Early stopping,
parameter decomposition
BibRef
Tatjer, A.[Albert],
Nagarajan, B.[Bhalaji],
Marques, R.[Ricardo],
Radeva, P.[Petia],
Decoding class dynamics in learning with noisy labels,
PRL(184), 2024, pp. 239-245.
Elsevier DOI Code:
WWW Link.
2408
Learning with Noisy Labels, Label Noise Modelling, Class Dynamics
BibRef
Yang, J.[Jie],
Niu, X.G.[Xiao-Guang],
Xu, Y.Z.[Yuan-Zhuo],
Zhang, Z.[Zejun],
Guo, G.Y.[Guang-Yi],
Drew, S.[Steve],
Chen, R.Z.[Rui-Zhi],
Dynamic selection for reconstructing instance-dependent noisy labels,
PR(156), 2024, pp. 110803.
Elsevier DOI
2408
Instance-dependent noise, Label reconstruction,
Sample selection, Identity mapping
BibRef
Lin, H.[Han],
Li, Y.J.[Ying-Jian],
Zhang, Z.[Zheng],
Zhu, L.[Lei],
Xu, Y.[Yong],
Learning With Noisy Labels by Semantic and Feature Space
Collaboration,
CirSysVideo(34), No. 8, August 2024, pp. 7190-7201.
IEEE DOI Code:
WWW Link.
2408
Noise measurement, Semantics, Prototypes, Self-supervised learning,
Collaboration, Training, Robustness, Label noise,
image classification
BibRef
Huang, Z.Y.[Zhen-Yu],
Hu, P.[Peng],
Niu, G.C.[Guo-Cheng],
Xiao, X.Y.[Xin-Yan],
Lv, J.C.[Jian-Cheng],
Peng, X.[Xi],
Learning with Noisy Correspondence,
IJCV(132), No. 1, January 2024, pp. 3656-3677.
Springer DOI
2409
BibRef
Wu, H.[Hao],
Sun, J.[Jun],
Robust Image Classification With Noisy Labels by Negative Learning
and Feature Space Renormalization,
MultMed(26), 2024, pp. 9280-9291.
IEEE DOI
2409
Noise measurement, Predictive models, Semisupervised learning,
Data models, Training, Image classification, Supervised learning,
multi-network architecture
BibRef
Zhang, Y.[Yue],
Chen, Y.[Yiyi],
Fang, C.W.[Chao-Wei],
Wang, Q.[Qian],
Wu, J.Y.[Jia-Yi],
Xin, J.[Jingmin],
Learning from open-set noisy labels based on multi-prototype modeling,
PR(157), 2025, pp. 110902.
Elsevier DOI Code:
WWW Link.
2409
Deep learning, Nosiy label, Out-of-distribution, Prototype learning
BibRef
Liu, Y.H.[Ya-Hui],
Wang, J.[Jian],
Yang, Y.[Yuntai],
Wang, R.[Renlong],
Wang, S.[Simiao],
Hierarchical Noise-Tolerant Meta-Learning With Noisy Labels,
SPLetters(31), 2024, pp. 3020-3024.
IEEE DOI
2411
Noise measurement, Optimization, Noise, Feature extraction,
Robustness, Metalearning, Cams, Training, Predictive models, Accuracy,
noisy labels
BibRef
Kou, Z.Q.[Zhi-Qiang],
Wang, J.[Jing],
Jia, Y.H.[Yu-Heng],
Geng, X.[Xin],
Inaccurate Label Distribution Learning,
CirSysVideo(34), No. 10, October 2024, pp. 10237-10249.
IEEE DOI
2411
Noise, Training, Lakes, Predictive models, Machine learning algorithms,
sparse
BibRef
Luo, W.S.[Wen-Shui],
Chen, S.[Shuo],
Liu, T.L.[Tong-Liang],
Han, B.[Bo],
Niu, G.[Gang],
Sugiyama, M.[Masashi],
Tao, D.C.[Da-Cheng],
Gong, C.[Chen],
Estimating Per-Class Statistics for Label Noise Learning,
PAMI(47), No. 1, January 2025, pp. 305-322.
IEEE DOI
2412
Noise measurement, Estimation, Noise, Training, Accuracy,
Classification algorithms, Matrix converters, Label noise, unbiasedness
BibRef
Cordeiro, F.R.[Filipe R.],
Carneiro, G.[Gustavo],
ANNE: Adaptive Nearest Neighbours and Eigenvector-based sample
selection for robust learning with noisy labels,
PR(159), 2025, pp. 111132.
Elsevier DOI Code:
WWW Link.
2412
Noisy label learning, Deep learning, Sample selection
BibRef
Feng, C.[Chen],
Tzimiropoulos, G.[Georgios],
Patras, I.[Ioannis],
NoiseBox: Toward More Efficient and Effective Learning With Noisy
Labels,
CirSysVideo(34), No. 11, November 2024, pp. 11914-11928.
IEEE DOI
2412
Noise, Noise measurement, Training, Computational modeling,
Predictive models, Supervised learning, Entropy, Noisy labels,
class imbalance
BibRef
Zhang, Y.Q.[You-Qiang],
Ding, R.[Ruihui],
Shi, H.[Hao],
Liu, J.X.[Jia-Xi],
Yu, Q.[Qiqiong],
Cao, G.[Guo],
Li, X.S.[Xue-Song],
Ensemble Network-Based Distillation for Hyperspectral Image
Classification in the Presence of Label Noise,
RS(16), No. 22, 2024, pp. 4247.
DOI Link
2412
BibRef
Zhang, Y.L.[Yi-Liang],
Lu, Y.[Yang],
Wang, H.Z.[Han-Zi],
Label-noise learning via uncertainty-aware neighborhood sample
selection,
PRL(186), 2024, pp. 191-197.
Elsevier DOI
2412
Label noise learning, Sample selection, Prediction bias
BibRef
Wu, Y.X.[Yi-Xin],
Xue, H.[Hui],
An, Y.[Yuexuan],
Fang, P.F.[Peng-Fei],
Learning Noisy Few-Shot Classification Without Relying on
Pseudo-Noise Data,
SPLetters(32), 2025, pp. 86-90.
IEEE DOI
2501
Noise measurement, Training, Prototypes, Smoothing methods, Noise,
Adaptation models, Robustness, Labeling, Feature extraction, model robustness
BibRef
Gu, S.L.[Shi-Lin],
Xu, C.[Chao],
Hu, D.[Dewen],
Hou, C.P.[Chen-Ping],
Adaptive Learning for Dynamic Features and Noisy Labels,
PAMI(47), No. 2, February 2025, pp. 1219-1237.
IEEE DOI
2501
Noise measurement, Noise, Data models, Heuristic algorithms,
Adaptation models, Training, Adaptive learning, Streams, noisy labels
BibRef
Yang, P.[Peiyu],
Akhtar, N.[Naveed],
Shah, M.[Mubarak],
Mian, A.[Ajmal],
Regulating Model Reliance on Non-robust Features by Smoothing Input
Marginal Density,
ECCV24(LVII: 329-347).
Springer DOI
2412
Code:
WWW Link.
BibRef
Xuan, X.[Xiwei],
Deng, Z.Q.[Zi-Quan],
Lin, H.T.[Hsuan-Tien],
Ma, K.L.[Kwan-Liu],
SLIM: Spuriousness Mitigation with Minimal Human Annotations,
ECCV24(XLVI: 215-231).
Springer DOI
2412
Code:
WWW Link.
BibRef
Huang, Q.Z.[Qing-Zheng],
He, X.L.[Xi-Lin],
Xian, X.L.[Xiao-Le],
Lin, Q.L.[Qin-Liang],
Xie, W.C.[Wei-Cheng],
Song, S.Y.[Si-Yang],
Shen, L.L.[Lin-Lin],
Yu, Z.T.[Zi-Tong],
Mtadcs: Moving Trace and Feature Density-based Confidence Sample
Selection Under Label Noise,
ECCV24(LXXI: 178-195).
Springer DOI
2412
BibRef
Gong, Y.F.[Yi-Fan],
Zhan, Z.[Zheng],
Li, Y.[Yanyu],
Idelbayev, Y.[Yerlan],
Zharkov, A.[Andrey],
Aberman, K.[Kfir],
Tulyakov, S.[Sergey],
Wang, Y.Z.[Yan-Zhi],
Ren, J.[Jian],
Efficient Training with Denoised Neural Weights,
ECCV24(LXXXIII: 18-34).
Springer DOI
2412
BibRef
Xu, J.Y.[Jing-Yi],
Le, H.[Hieu],
Samaras, D.[Dimitris],
Assessing Sample Quality via the Latent Space of Generative Models,
ECCV24(LIX: 449-464).
Springer DOI
2412
BibRef
Hoang, T.[Tuan],
Tran, H.[Hung],
Rana, S.[Santu],
Gupta, S.I.[Sun-Il],
Venkatesh, S.[Svetha],
Revisiting Sample Weights Based Method for Noisy-label Detection and
Classification,
ACCV24(I: 95-110).
Springer DOI
2412
BibRef
Baik, J.S.[Jae Soon],
Yoon, I.Y.[In Young],
Kim, K.H.[Kun Hoon],
Choi, J.W.[Jun Won],
Distribution-aware Robust Learning from Long-tailed Data with Noisy
Labels,
ECCV24(XIV: 160-177).
Springer DOI
2412
BibRef
Sheng, M.M.[Meng-Meng],
Sun, Z.[Zeren],
Chen, T.[Tao],
Pang, S.[Shuchao],
Wang, Y.C.[Yu-Cheng],
Yao, Y.Z.[Ya-Zhou],
Foster Adaptivity and Balance in Learning with Noisy Labels,
ECCV24(XXVII: 217-235).
Springer DOI
2412
BibRef
Di, Z.[Zonglin],
Zhu, Z.W.[Zhao-Wei],
Li, X.X.[Xiao-Xiao],
Liu, Y.[Yang],
Federated Learning with Local Openset Noisy Labels,
ECCV24(XXXIV: 38-56).
Springer DOI
2412
BibRef
Que, X.F.[Xiao-Fan],
Yu, Q.[Qi],
Optimal Transport of Diverse Unsupervised Tasks for Robust Learning
from Noisy Few-shot Data,
ECCV24(XXXIX: 294-311).
Springer DOI
2412
BibRef
Albert, P.[Paul],
Valmadre, J.[Jack],
Arazo, E.[Eric],
Krishna, T.[Tarun],
O'Connor, N.E.[Noel E.],
McGuinness, K.[Kevin],
An Accurate Detection Is Not All You Need to Combat Label Noise in
Web-noisy Datasets,
ECCV24(XLIX: 55-72).
Springer DOI
2412
BibRef
Kim, K.I.[Kwang In],
Distributed Active Client Selection With Noisy Clients Using Model
Association Scores,
ECCV24(LX: 75-92).
Springer DOI
2412
BibRef
Wang, S.Q.[Si-Qi],
Plummer, B.A.[Bryan A.],
Lnl+k: Enhancing Learning with Noisy Labels Through Noise Source
Knowledge Integration,
ECCV24(LXI: 374-392).
Springer DOI
2412
BibRef
Kim, S.[Suyeon],
Lee, D.[Dongha],
Kang, S.K.[Seong-Ku],
Chae, S.[Sukang],
Jang, S.[Sanghwan],
Yu, H.[Hwanjo],
Learning Discriminative Dynamics with Label Corruption for Noisy
Label Detection,
CVPR24(22477-22487)
IEEE DOI
2410
Training, Accuracy, Annotations, Noise, Robustness,
Noisy label detection, Noisy annotation
BibRef
Zhao, R.[Rui],
Shi, B.[Bin],
Ruan, J.F.[Jian-Fei],
Pan, T.Z.[Tian-Ze],
Dong, B.[Bo],
Estimating Noisy Class Posterior with Part-level Labels for Noisy
Label Learning,
CVPR24(22809-22819)
IEEE DOI
2410
Training, Computational modeling, Benchmark testing,
Noise measurement, Noisy Label Learning,
Self-supervised Learning
BibRef
Kim, N.R.[Noo-Ri],
Lee, J.S.[Jin-Seop],
Lee, J.H.[Jee-Hyong],
Learning with Structural Labels for Learning with Noisy Labels,
CVPR24(27600-27610)
IEEE DOI
2410
Manifolds, Training, Crowdsourcing, Costs, Noise,
Artificial neural networks, Robustness,
Structural Labels
BibRef
Radhakrishnan, A.[Aswathnarayan],
Davis, J.[Jim],
Rabin, Z.[Zachary],
Lewis, B.[Benjamin],
Scherreik, M.[Matthew],
Ilin, R.[Roman],
Design Choices for Enhancing Noisy Student Self-Training,
WACV24(1915-1924)
IEEE DOI
2404
Filtering, Source coding, Pipelines, Training data,
Self-supervised learning, Semisupervised learning
BibRef
Katsumata, K.[Kai],
Vo, D.M.[Duc Minh],
Harada, T.[Tatsuya],
Nakayama, H.[Hideki],
Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and
Uncurated Unlabeled Data,
WACV24(5311-5320)
IEEE DOI
2404
Training, Image synthesis, Noise, Computer architecture,
Generative adversarial networks, Data models, Algorithms,
image and video synthesis
BibRef
Wang, X.S.[Xiao-Song],
Xu, Z.Y.[Zi-Yue],
Yang, D.[Dong],
Tam, L.[Leo],
Roth, H.[Holger],
Xu, D.[Daguang],
Learning Quality Labels for Robust Image Classification,
WACV24(1092-1101)
IEEE DOI
2404
Training, Annotations, Supervised learning, MIMICs,
Noise measurement, Labeling, Task analysis, Algorithms,
Biomedical / healthcare / medicine
BibRef
Venkataramani, R.[Rahul],
Dutta, P.[Parag],
Melapudi, V.[Vikram],
Dukkipati, A.[Ambedkar],
Causal Feature Alignment:
Learning to Ignore Spurious Background Features,
WACV24(4654-4662)
IEEE DOI
2404
Location awareness, Image segmentation, Correlation, Annotations,
Image color analysis, Reviews, Algorithms, Explainable, fair
BibRef
Taraday, M.K.[Mitchell Keren],
Baskin, C.[Chaim],
Enhanced Meta Label Correction for Coping with Label Corruption,
ICCV23(16249-16258)
IEEE DOI
2401
BibRef
Yenamandra, S.[Sriram],
Ramesh, P.[Pratik],
Prabhu, V.[Viraj],
Hoffman, J.[Judy],
FACTS: First Amplify Correlations and Then Slice to Discover Bias,
ICCV23(4771-4781)
IEEE DOI Code:
WWW Link.
2401
BibRef
Ahn, C.[Chanho],
Kim, K.[Kikyung],
Baek, J.W.[Ji-Won],
Lim, J.[Jongin],
Han, S.J.[Seung-Ju],
Sample-wise Label Confidence Incorporation for Learning with Noisy
Labels,
ICCV23(1823-1832)
IEEE DOI
2401
BibRef
Yuan, S.[Suqin],
Feng, L.[Lei],
Liu, T.L.[Tong-Liang],
Late Stopping: Avoiding Confidently Learning from Mislabeled Examples,
ICCV23(16033-16042)
IEEE DOI
2401
BibRef
Huang, H.X.[Hua-Xi],
Kang, H.[Hui],
Liu, S.[Sheng],
Salvado, O.[Olivier],
Rakotoarivelo, T.[Thierry],
Wang, D.D.[Da-Dong],
Liu, T.L.[Tong-Liang],
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for
Learning with Noisy Labels,
ICCV23(16673-16684)
IEEE DOI
2401
BibRef
Yang, H.[Han],
Wang, T.Y.[Tian-Yu],
Hu, X.W.[Xiao-Wei],
Fu, C.W.[Chi-Wing],
SILT: Shadow-aware Iterative Label Tuning for Learning to Detect
Shadows from Noisy Labels,
ICCV23(12641-12652)
IEEE DOI
2401
BibRef
Xia, X.B.[Xiao-Bo],
Han, B.[Bo],
Zhan, Y.B.[Yi-Bing],
Yu, J.[Jun],
Gong, M.M.[Ming-Ming],
Gong, C.[Chen],
Liu, T.L.[Tong-Liang],
Combating Noisy Labels with Sample Selection by Mining
High-Discrepancy Examples,
ICCV23(1833-1843)
IEEE DOI
2401
BibRef
Zhang, M.[Manyi],
Zhao, X.Y.[Xu-Yang],
Yao, J.[Jun],
Yuan, C.[Chun],
Huang, W.[Weiran],
When Noisy Labels Meet Long Tail Dilemmas:
A Representation Calibration Method,
ICCV23(15844-15854)
IEEE DOI
2401
BibRef
Rabbani, N.[Navid],
Bartoli, A.[Adrien],
Unsupervised Confidence Approximation:
Trustworthy Learning from Noisy Labelled Data,
Uncertainty23(4611-4619)
IEEE DOI
2401
BibRef
Ricci, S.[Simone],
Uricchio, T.[Tiberio],
del Bimbo, A.[Alberto],
Smoothing and Transition Matrices Estimation to Learn with Noisy Labels,
CIAP23(I:450-462).
Springer DOI
WWW Link.
2312
BibRef
Joshi, A.[Aniket],
Das, N.[Nilotpal],
Yenigalla, P.[Promod],
Product Image Representation Learning on Large Scale Noisy Datasets,
ICIP23(2570-2574)
IEEE DOI
2312
BibRef
Pastor, A.[Andréas],
Krasula, L.[Lukáš],
Zhu, X.Q.[Xiao-Qing],
Li, Z.[Zhi],
Callet, P.L.[Patrick Le],
Recovering Quality Scores in Noisy Pairwise Subjective Experiments
Using Negative Log-Likelihood,
ICIP23(2635-2639)
IEEE DOI
2312
BibRef
Tai, T.M.[Tsung-Ming],
Jhang, Y.J.[Yun-Jie],
Hwang, W.J.[Wen-Jyi],
Robust Feature Learning Against Noisy Labels,
ICIP23(2235-2239)
IEEE DOI
2312
BibRef
Zhang, Y.Q.[Yong-Qi],
Zhang, H.[Hui],
Yao, Q.M.[Quan-Ming],
Wan, J.[Jun],
Combining Self-Supervised and Supervised Learning with Noisy Labels,
ICIP23(605-609)
IEEE DOI
2312
BibRef
Tallec, G.[Gauthier],
Yvinec, E.[Edouard],
Dapogny, A.[Arnaud],
Bailly, K.[Kevin],
Fighting Over-Fitting with Quantization for Learning Deep Neural
Networks on Noisy Labels,
ICIP23(575-579)
IEEE DOI
2312
BibRef
Yu, X.T.[Xiao-Tian],
Jiang, Y.[Yang],
Shi, T.Q.[Tian-Qi],
Feng, Z.L.[Zun-Lei],
Wang, Y.X.[Yue-Xuan],
Song, M.L.[Ming-Li],
Sun, L.[Li],
How to Prevent the Continuous Damage of Noises to Model Training?,
CVPR23(12054-12063)
IEEE DOI
2309
BibRef
Bucarelli, M.S.[Maria Sofia],
Cassano, L.[Lucas],
Siciliano, F.[Federico],
Mantrach, A.[Amin],
Silvestri, F.[Fabrizio],
Leveraging Inter-Rater Agreement for Classification in the Presence
of Noisy Labels,
CVPR23(3439-3448)
IEEE DOI
2309
BibRef
Huang, Z.Z.[Zhi-Zhong],
Zhang, J.P.[Jun-Ping],
Shan, H.M.[Hong-Ming],
Twin Contrastive Learning with Noisy Labels,
CVPR23(11661-11670)
IEEE DOI
2309
BibRef
Gan, B.[Bei],
Shu, X.J.[Xiu-Jun],
Qiao, R.Z.[Rui-Zhi],
Wu, H.Q.[Hao-Qian],
Chen, K.Y.[Ke-Yu],
Li, H.[Hanjun],
Ren, B.[Bo],
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for
Multi-modal Highlight Detection in Movies,
CVPR23(18898-18907)
IEEE DOI
2309
BibRef
Li, Y.F.[Yi-Fan],
Han, H.[Hu],
Shan, S.G.[Shi-Guang],
Chen, X.L.[Xi-Lin],
DISC: Learning from Noisy Labels via Dynamic Instance-Specific
Selection and Correction,
CVPR23(24070-24079)
IEEE DOI
2309
BibRef
Tatjer, A.[Albert],
Nagarajan, B.[Bhalaji],
Marques, R.[Ricardo],
Radeva, P.[Petia],
CCLM: Class-conditional Label Noise Modelling,
IbPRIA23(3-14).
Springer DOI
2307
BibRef
Tu, Y.P.[Yuan-Peng],
Zhang, B.S.[Bo-Shen],
Li, Y.X.[Yu-Xi],
Liu, L.[Liang],
Li, J.[Jian],
Zhang, J.N.[Jiang-Ning],
Wang, Y.[Yabiao],
Wang, C.J.[Cheng-Jie],
Zhao, C.R.[Cai Rong],
Learning with Noisy labels via Self-supervised Adversarial Noisy
Masking,
CVPR23(16186-16195)
IEEE DOI
2309
BibRef
And:
Earlier: A1, A2, A3, A4, A5, A7, A8, A9, Only:
Learning from Noisy Labels with Decoupled Meta Label Purifier,
CVPR23(19934-19943)
IEEE DOI
2309
BibRef
Zhang, B.S.[Bo-Shen],
Li, Y.X.[Yu-Xi],
Tu, Y.P.[Yuan-Peng],
Peng, J.L.[Jin-Long],
Wang, Y.B.[Ya-Biao],
Wu, C.[Cunlin],
Xiao, Y.[Yang],
Zhao, C.R.[Cai-Rong],
Learning from Noisy Labels with Coarse-to-fine Sample Credibility
Modeling,
LLID22(21-38).
Springer DOI
2304
BibRef
Yang, C.H.H.[Chao-Han Huck],
Hung, D.I.T.[Danny I-Te],
Liu, Y.C.[Yi-Chieh],
Chen, P.Y.[Pin-Yu],
Treatment Learning Causal Transformer for Noisy Image Classification,
WACV23(6128-6139)
IEEE DOI
2302
Deep learning, Visualization, Correlation, Perturbation methods,
Training data, Benchmark testing, Transformers
BibRef
Garg, A.[Arpit],
Nguyen, C.[Cuong],
Felix, R.[Rafael],
Do, T.T.[Thanh-Toan],
Carneiro, G.[Gustavo],
Instance-dependent Noisy-label Learning with Graphical Model Based
Noise-rate Estimation,
ECCV24(IV: 372-389).
Springer DOI
2412
BibRef
Earlier:
Instance-Dependent Noisy Label Learning via Graphical Modelling,
WACV23(2287-2297)
IEEE DOI
2302
Training, Deep learning, Visualization, Biological system modeling,
Ecosystems, Benchmark testing, and algorithms (including transfer)
BibRef
Smart, B.[Brandon],
Carneiro, G.[Gustavo],
Bootstrapping the Relationship Between Images and Their Clean and
Noisy Labels,
WACV23(5333-5343)
IEEE DOI
2302
Training, Computational modeling, Semisupervised learning,
Prediction algorithms, Data models, visual reasoning
BibRef
Liu, J.[Jiarun],
Jiang, D.G.[Da-Guang],
Yang, Y.K.[Yu-Kun],
Li, R.R.[Rui-Rui],
Agreement or Disagreement in Noise-tolerant Mutual Learning?,
ICPR22(4801-4807)
IEEE DOI
2212
Training, Deep learning, Codes, Collaboration, Resists, Robustness
BibRef
Fu, X.M.[Xuan-Ming],
Yang, Z.F.[Zheng-Feng],
Xue, H.[Hao],
Wang, J.L.[Jian-Lin],
Zeng, Z.B.[Zhen-Bing],
Robust Training with Feature-Based Adversarial Example,
ICPR22(2957-2963)
IEEE DOI
2212
Training, Perturbation methods, Robustness
BibRef
Li, J.C.[Ji-Chang],
Li, G.B.[Guan-Bin],
Liu, F.[Feng],
Yu, Y.Z.[Yi-Zhou],
Neighborhood Collective Estimation for Noisy Label Identification and
Chiorrection,
ECCV22(XXIV:128-145).
Springer DOI
2211
BibRef
Gao, Z.Q.[Zheng-Qi],
Sun, F.K.[Fan-Keng],
Yang, M.[Mingran],
Ren, S.C.[Su-Cheng],
Xiong, Z.K.[Zi-Kai],
Engeler, M.[Marc],
Burazer, A.[Antonio],
Wildling, L.[Linda],
Daniel, L.[Luca],
Boning, D.S.[Duane S.],
Learning from Multiple Annotator Noisy Labels via Sample-Wise Label
Fusion,
ECCV22(XXIV:407-422).
Springer DOI
2211
BibRef
Wei, Q.[Qi],
Sun, H.L.[Hao-Liang],
Lu, X.[Xiankai],
Yin, Y.L.[Yi-Long],
Self-Filtering: A Noise-Aware Sample Selection for Label Noise with
Confidence Penalization,
ECCV22(XXX:516-532).
Springer DOI
2211
BibRef
Kim, Y.[Youngeun],
Fang, J.[Jun],
Zhang, Q.[Qin],
Cai, Z.W.[Zhao-Wei],
Shen, Y.[Yantao],
Duggal, R.[Rahul],
Raychaudhuri, D.S.[Dripta S.],
Tu, Z.W.[Zhuo-Wen],
Xing, Y.F.[Yi-Fan],
Dabeer, O.[Onkar],
Open-world Dynamic Prompt and Continual Visual Representation Learning,
ECCV24(XLIX: 357-374).
Springer DOI
2412
BibRef
Cao, T.Y.[Tian-Yue],
Wang, Y.X.[Yong-Xin],
Xing, Y.F.[Yi-Fan],
Xiao, T.J.[Tian-Jun],
He, T.[Tong],
Zhang, Z.[Zheng],
Zhou, H.[Hao],
Tighe, J.[Joseph],
PSS: Progressive Sample Selection for Open-World Visual Representation
Learning,
ECCV22(XXXI:278-294).
Springer DOI
2211
BibRef
Kim, K.I.[Kwang In],
Active Label Correction Using Robust Parameter Update and Entropy
Propagation,
ECCV22(XXI:1-16).
Springer DOI
2211
BibRef
Zhang, Z.[Ziyi],
Chen, W.[Weikai],
Fang, C.W.[Chao-Wei],
Li, Z.[Zhen],
Chen, L.[Lechao],
Lin, L.[Liang],
Li, G.B.[Guan-Bin],
RankMatch: Fostering Confidence and Consistency in Learning with
Noisy Labels,
ICCV23(1644-1654)
IEEE DOI
2401
BibRef
Zhao, G.L.[Gan-Long],
Li, G.B.[Guan-Bin],
Qin, Y.P.[Yi-Peng],
Liu, F.[Feng],
Yu, Y.Z.[Yi-Zhou],
Centrality and Consistency: Two-Stage Clean Samples Identification for
Learning with Instance-Dependent Noisy Labels,
ECCV22(XXV:21-37).
Springer DOI
2211
BibRef
Kye, S.M.[Seong Min],
Choi, K.[Kwanghee],
Yi, J.Y.[Joon-Young],
Chang, B.[Buru],
Learning with Noisy Labels by Efficient Transition Matrix Estimation to
Combat Label Miscorrection,
ECCV22(XXV:717-738).
Springer DOI
2211
BibRef
Gao, Z.Y.[Zi-Yang],
Yan, Y.P.[Ya-Ping],
Geng, X.[Xin],
Learning from Noisy Labels via Meta Credible Label Elicitation,
ICIP22(1391-1395)
IEEE DOI
2211
Degradation, Correlation, Neural networks, Benchmark testing,
Robustness, Noise measurement, Meta Credible Label Elicitation,
Label distribution
BibRef
Gao, B.Y.[Bo-Yan],
Gouk, H.[Henry],
Hospedales, T.M.[Timothy M.],
Searching for Robustness:
Loss Learning for Noisy Classification Tasks,
ICCV21(6650-6659)
IEEE DOI
2203
Training, Training data,
Network architecture, Search problems, Robustness,
Machine learning architectures and formulations
BibRef
Wu, Z.F.[Zhi-Fan],
Wei, T.[Tong],
Jiang, J.W.[Jian-Wen],
Mao, C.J.[Chao-Jie],
Tang, M.Q.[Ming-Qian],
Li, Y.F.[Yu-Feng],
NGC: A Unified Framework for Learning with Open-World Noisy Data,
ICCV21(62-71)
IEEE DOI
2203
Training, Degradation, Prototypes, Machine learning, Detectors,
Predictive models, Recognition and classification,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Zhou, X.[Xiong],
Liu, X.M.[Xian-Ming],
Wang, C.Y.[Chen-Yang],
Zhai, D.[Deming],
Jiang, J.J.[Jun-Jun],
Ji, X.Y.[Xiang-Yang],
Learning with Noisy Labels via Sparse Regularization,
ICCV21(72-81)
IEEE DOI
2203
Training, Deep learning, Neural networks, Fitting, Robustness, Entropy,
Recognition and classification, Efficient training and inference methods
BibRef
Yao, Y.Z.[Ya-Zhou],
Sun, Z.[Zeren],
Zhang, C.Y.[Chuan-Yi],
Shen, F.M.[Fu-Min],
Wu, Q.[Qi],
Zhang, J.[Jian],
Tang, Z.M.[Zhen-Min],
Jo-SRC: A Contrastive Approach for Combating Noisy Labels,
CVPR21(5188-5197)
IEEE DOI
2111
Training, Degradation, Deep learning, Codes,
Robustness
BibRef
Goel, P.[Purvi],
Chen, L.[Li],
On the Robustness of Monte Carlo Dropout Trained with Noisy Labels,
WiCV21(2219-2228)
IEEE DOI
2109
Training, Uncertainty, Monte Carlo methods, Neurons, Estimation,
Robustness
BibRef
Wei, H.,
Feng, L.,
Chen, X.,
An, B.,
Combating Noisy Labels by Agreement:
A Joint Training Method with Co-Regularization,
CVPR20(13723-13732)
IEEE DOI
2008
Noise measurement, Training, Robustness, Semisupervised learning,
Neural networks, Machine learning, Estimation
BibRef
Zhang, W.H.[Wei-He],
Wang, Y.[Yali],
Qiao, Y.[Yu],
MetaCleaner: Learning to Hallucinate Clean Representations for
Noisy-Labeled Visual Recognition,
CVPR19(7365-7374).
IEEE DOI
2002
BibRef
Li, Y.C.[Yun-Cheng],
Yang, J.C.[Jian-Chao],
Song, Y.[Yale],
Cao, L.L.[Liang-Liang],
Luo, J.B.[Jie-Bo],
Li, L.J.[Li-Jia],
Learning from Noisy Labels with Distillation,
ICCV17(1928-1936)
IEEE DOI
1802
graph theory, image recognition,
learning (artificial intelligence), statistical analysis, Visualization
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Outlier Detection and Analysis, Robust Analysis, Out of Distribution, OOD .