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We propose a simulation framework for generating instance-dependent noisy labels via a pseudo-labeling paradigm.
Learning from noisy labels with distillation
Y. Li, J. Yang, Y. Song, L. Cao, J. Luo, and L.-J. Li · 1918
Earlier work this paper cites.
Maximum likelihood estimation of observer error-rates using the EM algorithm
A. P. Dawid and A. M. Skene · 1979
Earlier work this paper cites.
Learning from noisy examples
D. Angluin and P. Laird · 1988
Earlier work this paper cites.
A critical investigation of recall and precision as measures of retrieval system performance
V. Raghavan, P. Bollmann, and G. S. Jung · 1989
Earlier work this paper cites.
A decision-theoretic generalization of on-line learning and an application to boosting
Y. Freund and R. E. Schapire · 1997
Earlier work this paper cites.
Ensemble methods in machine learning
T. G. Dietterich · 2000
Earlier work this paper cites.
DivideMix: Learning with noisy labels as semi-supervised learning
J. Li, R. Socher, and S. C. Hoi · 2002
Earlier work this paper cites.
Learning object evaluation: computer-mediated collaboration and inter-rater reliability
J. Vargo, J. C. Nesbit, K. Belfer, and A. Archambault · 2003
Earlier work this paper cites.
Model compression
C. Buciluǎ, R. Caruana, and A. Niculescu-Mizil · 2006
Earlier work this paper cites.
Semi-Supervised Learning
O. Chapelle, B. Schölkopf, and A. Zien · 2006
Earlier work this paper cites.
Asirra: a CAPTCHA that exploits interest-aligned manual image categorization
J. Elson, J. R. Douceur, J. Howell, and J. Saul · 2007
Earlier work this paper cites.
Answering the call for a standard reliability measure for coding data
A. F. Hayes and K. Krippendorff · 2007
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Average precision
E. Zhang and Y. Zhang · 2009
Earlier work this paper cites.
Learning from crowds
V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, and L. Moy · 2010
Earlier work this paper cites.
Pairwise ranking aggregation in a crowdsourced setting
X. Chen, P. N. Bennett, K. Collins-Thompson, and E. Horvitz · 2013
Earlier work this paper cites.
Item response theory
S. E. Embretson and S. P. Reise · 2013
Earlier work this paper cites.
Inferring ground truth from multi-annotator ordinal data: a probabilistic approach
B. Lakshminarayanan and Y. W. Teh · 2013
Earlier work this paper cites.
Learning with noisy labels
N. Natarajan, I. S. Dhillon, P. Ravikumar, and A. Tewari · 2013
Earlier work this paper cites.
Training deep neural networks on noisy labels with bootstrapping
S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, and A. Rabinovich · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Dynamic estimation of worker reliability in crowdsourcing for regression tasks: Making it work
A. Tarasov, S. J. Delany, and B. Mac Namee · 2014
Earlier work this paper cites.
Spectral methods meet EM: A provably optimal algorithm for crowdsourcing
Y. Zhang, X. Chen, D. Zhou, and M. I. Jordan · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Learning from massive noisy labeled data for image classification
T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
The unreasonable effectiveness of noisy data for fine-grained recognition
J. Krause, B. Sapp, A. Howard, H. Zhou, A. Toshev, T. Duerig, J. Philbin, and L. Fei-Fei · 2016
Cited alongside, same era.
Data programming: Creating large training sets, quickly
Rotation equivariant CNNs for digital pathology
B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, and M. Welling · 2018
Later among the works it cites.
Iterative learning with open-set noisy labels
Y. Wang, W. Liu, X. Ma, J. Bailey, H. Zha, L. Song, and S.-T. Xia · 2018
Later among the works it cites.
Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2018
Later among the works it cites.
Reconciling modern machine-learning practice and the classical bias–variance trade-off
M. Belkin, D. Hsu, S. Ma, and S. Mandal · 2019
Later among the works it cites.
Nuanced metrics for measuring unintended bias with real data for text classification
D. Borkan, L. Dixon, J. Sorensen, N. Thain, and L. Vasserman · 2019
Later among the works it cites.
Using pre-training can improve model robustness and uncertainty
D. Hendrycks, K. Lee, and M. Mazeika · 2019
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A. Ratner, C. De Sa, S. Wu, D. Selsam, and C. Ré · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Cited alongside, same era.
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
B. E. Bejnordi, M. Veta, P. J. Van Diest, B. Van Ginneken, N. Karssemeijer, G. Litjens, J. A. Van Der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, et al · 2017
Cited alongside, same era.
MobileNets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
Learning from noisy singly-labeled data
A. Khetan, Z. C. Lipton, and A. Anandkumar · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
B. Lakshminarayanan, A. Pritzel, and C. Blundell · 2017
Cited alongside, same era.
Decoupling “when to update” from “how to update”
E. Malach and S. Shalev-Shwartz · 2017
Cited alongside, same era.
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Robust inference via generative classifiers for handling noisy labels
K. Lee, S. Yun, K. Lee, H. Lee, B. Li, and J. Shin · 2019
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Improving accuracy and lowering cost in crowdsourcing through an unsupervised expertise estimation approach
A. Moayedikia, W. Yeoh, K.-L. Ong, and Y. L. Boo · 2019
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Human uncertainty makes classification more robust
J. C. Peterson, R. M. Battleday, T. L. Griffiths, and O. Russakovsky · 2019
Later among the works it cites.
Combinatorial inference against label noise
P. H. Seo, G. Kim, and B. Han · 2019
Later among the works it cites.
Countering noisy labels by learning from auxiliary clean labels
T. W. Tsai, C. Li, and J. Zhu · 2019
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Symmetric cross entropy for robust learning with noisy labels
Y. Wang, X. Ma, Z. Chen, Y. Luo, J. Yi, and J. Bailey · 2019
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Unsupervised data augmentation for consistency training
Q. Xie, Z. Dai, E. Hovy, M.-T. Luong, and Q. V. Le · 2019
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High-dimensional dynamics of generalization error in neural networks
M. S. Advani, A. M. Saxe, and H. Sompolinsky · 2020
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The elephant in the machine: Proposing a new metric of data reliability and its application to a medical case to assess classification reliability
F. Cabitza, A. Campagner, D. Albano, A. Aliprandi, A. Bruno, V. Chianca, A. Corazza, F. Di Pietto, A. Gambino, S. Gitto, et al · 2020
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A simple probabilistic method for deep classification under input-dependent label noise
M. Collier, B. Mustafa, E. Kokiopoulou, R. Jenatton, and J. Berent · 2020
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Underspecification presents challenges for credibility in modern machine learning
A. D’Amour, K. Heller, D. Moldovan, B. Adlam, B. Alipanahi, A. Beutel, C. Chen, J. Deaton, J. Eisenstein, M. D. Hoffman, et al · 2020
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Beyond synthetic noise: Deep learning on controlled noisy labels
L. Jiang, D. Huang, M. Liu, and W. Yang · 2020
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Strength from weakness: Fast learning using weak supervision
J. Robinson, S. Jegelka, and S. Sra · 2020
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FixMatch: Simplifying semi-supervised learning with consistency and confidence
K. Sohn, D. Berthelot, C.-L. Li, Z. Zhang, N. Carlini, E. D. Cubuk, A. Kurakin, H. Zhang, and C. Raffel · 2020
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Self-training with noisy student improves ImageNet classification
Q. Xie, M.-T. Luong, E. Hovy, and Q. V. Le · 2020
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Confidence scores make instance-dependent label-noise learning possible
A. Berthon, B. Han, G. Niu, T. Liu, and M. Sugiyama · 2021
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A second-order approach to learning with instance-dependent label noise
Z. Zhu, T. Liu, and Y. Liu · 2021
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