Liu, T.,
Tao, D.,
Classification with Noisy Labels by Importance Reweighting,
PAMI(38), No. 3, March 2016, pp. 447-461.
IEEE DOI
1602
Algorithm design and analysis.
Sample labels are randomly corrupted.
BibRef
Zhang, R.[Rui],
Chen, Z.H.[Zheng-Hao],
Zhang, S.X.[San-Xing],
Song, F.[Fei],
Zhang, G.[Gang],
Zhou, Q.C.[Quan-Cheng],
Lei, T.[Tao],
Remote Sensing Image Scene Classification with Noisy Label
Distillation,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link
2008
Training CNN requires lots of clean data.
BibRef
Gu, S.W.[Song-Wei],
Zhang, R.[Rui],
Luo, H.X.[Hong-Xia],
Li, M.Y.[Meng-Yao],
Feng, H.[Huamei],
Tang, X.G.[Xu-Guang],
Improved SinGAN Integrated with an Attentional Mechanism for Remote
Sensing Image Classification,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
2105
BibRef
Zhang, Q.[Qian],
Lee, F.F.[Fei-Fei],
Wang, Y.G.[Ya-Gang],
Miao, R.[Ran],
Chen, L.[Lei],
Chen, Q.[Qiu],
An improved noise loss correction algorithm for learning from noisy
labels,
JVCIR(72), 2020, pp. 102930.
Elsevier DOI
2010
Noisy labels, KL entropy, Mix-up loss, DNN
BibRef
de Aquino Afonso, B.K.[Bruno Klaus],
Berton, L.[Lilian],
Identifying noisy labels with a transductive semi-supervised
leave-one-out filter,
PRL(140), 2020, pp. 127-134.
Elsevier DOI
2012
Semi-supervised learning, Classification, Graph-based, Filter, Label noise
BibRef
Han, Y.[Yan],
Roy, S.K.[Soumava Kumar],
Petersson, L.[Lars],
Harandi, M.[Mehrtash],
Discrepant collaborative training by Sinkhorn divergences,
IVC(112), 2021, pp. 104213.
Elsevier DOI
2107
BibRef
Earlier:
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
BibRef
Sun, H.L.[Hao-Liang],
Guo, C.H.[Chen-Hui],
Wei, Q.[Qi],
Han, Z.Y.[Zhong-Yi],
Yin, Y.L.[Yi-Long],
Learning to rectify for robust learning with noisy labels,
PR(124), 2022, pp. 108467.
Elsevier DOI
2203
Label noise, Meta-learning, Probabilistic model, Robust learning
BibRef
Algan, G.[Görkem],
Ulusoy, I.[Ilkay],
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
BibRef
Yi, R.[Rumeng],
Huang, Y.P.[Ya-Ping],
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
BibRef
Nomura, Y.[Yuichiro],
Kurita, T.[Takio],
Sample Selection Approach with Number of False Predictions for Learning
with Noisy Labels,
IEICE(E105-D), No. 10, October 2022, pp. 1759-1768.
WWW Link.
2210
BibRef
Wang, S.[Shaoru],
Gao, J.[Jin],
Li, B.[Bing],
Hu, W.M.[Wei-Ming],
Narrowing the Gap:
Improved Detector Training With Noisy Location Annotations,
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
BibRef
Xu, Z.[Zhe],
Lu, D.[Donghuan],
Luo, J.[Jie],
Wang, Y.X.[Yi-Xin],
Yan, J.P.[Jiang-Peng],
Ma, K.[Kai],
Zheng, Y.F.[Ye-Feng],
Tong, R.K.Y.[Raymond Kai-Yu],
Anti-Interference From Noisy Labels: Mean-Teacher-Assisted Confident
Learning for Medical Image Segmentation,
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
BibRef
Zhang, L.[Le],
Tanno, R.[Ryutaro],
Xu, M.[Moucheng],
Huang, Y.[Yawen],
Bronik, K.[Kevin],
Jin, C.[Chen],
Jacob, J.[Joseph],
Zheng, Y.F.[Ye-Feng],
Shao, L.[Ling],
Ciccarelli, O.[Olga],
Barkhof, F.[Frederik],
Alexander, D.C.[Daniel C.],
Learning from multiple annotators for medical image segmentation,
PR(138), 2023, pp. 109400.
Elsevier DOI
2303
Multi-Annotator, Label fusion, Segmentation
BibRef
Li, H.[Hui],
Niu, Z.[Zhaodong],
Sun, Q.[Quan],
Li, Y.[Yabo],
Co-Correcting: Combat Noisy Labels in Space Debris Detection,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link
2211
BibRef
Miao, Q.[Qing],
Wu, X.H.[Xiao-He],
Xu, C.[Chao],
Zuo, W.M.[Wang-Meng],
Meng, Z.P.[Zhao-Peng],
On better detecting and leveraging noisy samples for learning with
severe label noise,
PR(136), 2023, pp. 109210.
Elsevier DOI
2301
Severe label noise, Lipschitz regularization,
Adaptive modeling and detection of label noise, Semi-supervised learning
BibRef
Gong, C.[Chen],
Ding, Y.L.[Yong-Liang],
Han, B.[Bo],
Niu, G.[Gang],
Yang, J.[Jian],
You, J.[Jane],
Tao, D.C.[Da-Cheng],
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
BibRef
Xia, X.B.[Xiao-Bo],
Han, B.[Bo],
Wang, N.N.[Nan-Nan],
Deng, J.K.[Jian-Kang],
Li, J.[Jiatong],
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
BibRef
Li, S.[Shikun],
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
BibRef
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
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 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.[Sucheng],
Xiong, Z.[Zikai],
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
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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
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
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.[Ziyang],
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, Pattern recognition
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, Pattern recognition
BibRef
Algan, G.[Görkem],
Ulusoy, I.[Ilkay],
Meta Soft Label Generation for Noisy Labels,
ICPR21(7142-7148)
IEEE DOI
2105
Training, Degradation, Adaptation models, Neural networks, Clothing,
Training data, deep learning, label noise, meta-learning
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 .