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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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WACV20(3158-3167)
IEEE DOI
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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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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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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],
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Luo, J.[Jie],
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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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PR(138), 2023, pp. 109400.
Elsevier DOI
2303
Multi-Annotator, Label fusion, Segmentation
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2301
Severe label noise, Lipschitz regularization,
Adaptive modeling and detection of label noise, Semi-supervised learning
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Gong, C.[Chen],
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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],
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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],
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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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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
BibRef
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],
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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],
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Li, G.[Ge],
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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],
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Generative Reasoning Integrated Label Noise Robust Deep Image
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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],
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Yin, Y.L.[Yi-Long],
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PAMI(45), No. 9, September 2023, pp. 11053-11066.
IEEE DOI
2309
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Noisy Label Learning With Provable Consistency for a Wider Family of
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PAMI(45), No. 11, November 2023, pp. 13536-13552.
IEEE DOI
2310
BibRef
Liu, F.C.[Fu-Chang],
Wang, Y.[Yu],
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GEIKD: Self-knowledge distillation based on gated ensemble networks
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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],
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A balanced random learning strategy for CNN based Landsat image
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PR(144), 2023, pp. 109824.
Elsevier DOI
2310
Landsat image segmentation, Noisy labels, Confidence interval,
Random learning, Multi-layer features
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Yan, Y.[Yan],
Xu, Y.[Youze],
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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],
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Zhang, S.L.[Shi-Liang],
Multi-proxy feature learning for robust fine-grained visual
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PR(143), 2023, pp. 109779.
Elsevier DOI
2310
Fine-grained visual recognition, Noisy label, Long tail, Proxy learning
BibRef
Liu, C.Y.[Cheng-Yang],
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RS(15), No. 20, 2023, pp. 4994.
DOI Link
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PR(144), 2023, pp. 109835.
Elsevier DOI
2310
Label noise, Dynamics, Robust loss function
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Yang, S.[Shuo],
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PAMI(45), No. 12, December 2023, pp. 14055-14068.
IEEE DOI
2311
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Wan, J.[Jia],
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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],
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A general elevating framework for label noise filters,
PR(147), 2024, pp. 110072.
Elsevier DOI
2312
Classification, Label noise, Noise filter, Sample reduction
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Feng, Z.[Zerun],
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Learning From Noisy Correspondence With Tri-Partition for Cross-Modal
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MultMed(26), 2024, pp. 3884-3896.
IEEE DOI
2402
Noise measurement, Semantics, Training, Semisupervised learning, Data models,
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Machine learning for low signal-to-noise ratio detection,
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Elsevier DOI
2403
Anomaly, artificial intelligence, Long short-term memory,
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ICCV23(4771-4781)
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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
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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
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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
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ICCV23(1833-1843)
IEEE DOI
2401
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Zhang, M.[Manyi],
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Yao, J.[Jun],
Yuan, C.[Chun],
Huang, W.[Weiran],
When Noisy Labels Meet Long Tail Dilemmas:
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ICCV23(15844-15854)
IEEE DOI
2401
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Bartoli, A.[Adrien],
Unsupervised Confidence Approximation:
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Uncertainty23(4611-4619)
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2401
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Product Image Representation Learning on Large Scale Noisy Datasets,
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2312
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Krasula, L.[Luká],
Zhu, X.Q.[Xiao-Qing],
Li, Z.[Zhi],
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Recovering Quality Scores in Noisy Pairwise Subjective Experiments
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ICIP23(2635-2639)
IEEE DOI
2312
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Yamaguchi, S.[Shin'Ya],
Kumagai, A.[Atsutoshi],
Covariance-Aware Feature Alignment with Pre-Computed Source
Statistics for Test-Time Adaptation to Multiple Image Corruptions,
ICIP23(800-804)
IEEE DOI
2312
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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
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Zhang, Y.Q.[Yong-Qi],
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Yao, Q.M.[Quan-Ming],
Wan, J.[Jun],
Combining Self-Supervised and Supervised Learning with Noisy Labels,
ICIP23(605-609)
IEEE DOI
2312
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Tallec, G.[Gauthier],
Yvinec, E.[Edouard],
Dapogny, A.[Arnaud],
Bailly, K.[Kevin],
Fighting Over-Fitting with Quantization for Learning Deep Neural
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ICIP23(575-579)
IEEE DOI
2312
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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
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Bucarelli, M.S.[Maria Sofia],
Cassano, L.[Lucas],
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CVPR23(3439-3448)
IEEE DOI
2309
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Huang, Z.Z.[Zhi-Zhong],
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Shan, H.M.[Hong-Ming],
Twin Contrastive Learning with Noisy Labels,
CVPR23(11661-11670)
IEEE DOI
2309
BibRef
Feng, C.[Chuanwen],
Ren, Y.L.[Yi-Long],
Xie, X.[Xike],
OT-Filter: An Optimal Transport Filter for Learning with Noisy Labels,
CVPR23(16164-16174)
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,
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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
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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
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Garg, A.[Arpit],
Nguyen, C.[Cuong],
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Instance-Dependent Noisy Label Learning via Graphical Modelling,
WACV23(2287-2297)
IEEE DOI
2302
Training, Deep learning, Visualization, Biological system modeling,
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Smart, B.[Brandon],
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WACV23(5333-5343)
IEEE DOI
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Training, Computational modeling, Semisupervised learning,
Prediction algorithms, Data models, visual reasoning
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Liu, J.[Jiarun],
Jiang, D.G.[Da-Guang],
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Agreement or Disagreement in Noise-tolerant Mutual Learning?,
ICPR22(4801-4807)
IEEE DOI
2212
Training, Deep learning, Codes, Collaboration, Resists, Robustness
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Fu, X.M.[Xuan-Ming],
Yang, Z.F.[Zheng-Feng],
Xue, H.[Hao],
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Robust Training with Feature-Based Adversarial Example,
ICPR22(2957-2963)
IEEE DOI
2212
Training, Perturbation methods, Robustness
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Li, J.C.[Ji-Chang],
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Liu, F.[Feng],
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Neighborhood Collective Estimation for Noisy Label Identification and
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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],
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Learning from Multiple Annotator Noisy Labels via Sample-Wise Label
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ECCV22(XXIV:407-422).
Springer DOI
2211
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Wei, Q.[Qi],
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Lu, X.[Xiankai],
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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
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, 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
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 .