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Recent semi-supervised learning methods use pseudo supervision as core idea, especially self-training methods that generate pseudo labels.
Learning from noisy examples,
D. Angluin, P. Laird, · 1988
Earlier work this paper cites.
Unsupervised word sense disambiguation rivaling supervised methods,
D. Yarowsky, · 1995
Earlier work this paper cites.
On mixup training: Improved calibration and predictive uncertainty for deep neural networks,
S. Thulasidasan, G. Chennupati, J. A. Bilmes, T. Bhattacharya, S. Michalak, · 2004
Earlier work this paper cites.
Semi-supervised learning by entropy minimization,
Y. Grandvalet, Y. Bengio, · 2005
Earlier work this paper cites.
Curriculum learning,
Y. Bengio, J. Louradour, R. Collobert, J. Weston, · 2009
Earlier work this paper cites.
A. Krizhevsky, Learning multiple layers of features from tiny images, Technical Report, University of Toronto, 2009
2009
Earlier work this paper cites.
Semi-supervised learning by disagreement,
Z.-H. Zhou, M. Li, · 2010
Earlier work this paper cites.
Semantic contours from inverse detectors,
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, J. Malik, · 2011
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,
D.-H. Lee, · 2013
Earlier work this paper cites.
The pascal visual object classes challenge: A retrospective,
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, A. Zisserman, · 2015
Earlier work this paper cites.
Wide residual networks,
S. Zagoruyko, N. Komodakis, · 2016
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding,
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, B. Schiele, · 2016
Earlier work this paper cites.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille, · 2017
Earlier work this paper cites.
Pyramid scene parsing network,
H. Zhao, J. Shi, X. Qi, X. Wang, J. Jia, · 2017
Earlier work this paper cites.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,
A. Tarvainen, H. Valpola, · 2017
Cited alongside, same era.
Training deep neural-networks using a noise adaptation layer,
J. Goldberger, E. Ben-Reuven, · 2017
Cited alongside, same era.
Decoupling "when to update" from "how to update",
E. Malach, S. Shalev-Shwartz, · 2017
Cited alongside, same era.
Temporal ensembling for semi-supervised learning,
S. Laine, T. Aila, · 2017
Cited alongside, same era.
Adversarial learning for semi-supervised semantic segmentation,
W. Hung, Y. Tsai, Y. Liou, Y. Lin, M. Yang, · 2018
Cited alongside, same era.
Unsupervised domain adaptation for semantic segmentation via class-balanced self-training,
Y. Zou, Z. Yu, B. Vijaya Kumar, J. Wang, · 2018
Semi-supervised semantic segmentation with high- and low-level consistency,
S. Mittal, M. Tatarchenko, T. Brox, · 2019
Later among the works it cites.
Dual student: Breaking the limits of the teacher in semi-supervised learning,
Z. Ke, D. Wang, Q. Yan, J. Ren, R. W. Lau, · 2019
Later among the works it cites.
How does disagreement help generalization against label corruption?,
X. Yu, B. Han, J. Yao, G. Niu, I. W. Tsang, M. Sugiyama, · 2019
Later among the works it cites.
Confidence regularized self-training,
Y. Zou, Z. Yu, X. Liu, B. Kumar, J. Wang, · 2019
Later among the works it cites.
S4l: Self-supervised semi-supervised learning,
X. Zhai, A. Oliver, A. Kolesnikov, L. Beyer, · 2019
Later among the works it cites.
Semi-supervised semantic segmentation needs strong, varied perturbations,
G. French, S. Laine, T. Aila, M. Mackiewicz, G. Finlayson, · 2020
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Cited alongside, same era.
Deep co-training for semi-supervised image recognition,
S. Qiao, W. Shen, Z. Zhang, B. Wang, A. Yuille, · 2018
Cited alongside, same era.
Co-teaching: Robust training of deep neural networks with extremely noisy labels,
B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, M. Sugiyama, · 2018
Cited alongside, same era.
Realistic evaluation of deep semi-supervised learning algorithms,
A. Oliver, A. Odena, C. A. Raffel, E. D. Cubuk, I. Goodfellow, · 2018
Cited alongside, same era.
Mixed precision training,
P. Micikevicius, S. Narang, J. Alben, G. F. Diamos, E. Elsen, D. García, B. Ginsburg, M. Houston, O. Kuchaiev, G. Venkatesh, H. Wu, · 2018
Cited alongside, same era.
Averaging weights leads to wider optima and better generalization,
P. Izmailov, D. Podoprikhin, T. Garipov, D. Vetrov, A. G. Wilson, · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization,
H. Zhang, M. Cisse, Y. N. Dauphin, D. Lopez-Paz, · 2018
Cited alongside, same era.
Closest in time.
Semi-supervised semantic segmentation with cross-consistency training,
Y. Ouali, C. Hudelot, M. Tami, · 2020
Closest in time.
Deep co-training for semi-supervised image segmentation,
J. Peng, G. Estrada, M. Pedersoli, C. Desrosiers, · 2020
Closest in time.
Guided collaborative training for pixel-wise semi-supervised learning,
Z. Ke, D. Qiu, K. Li, Q. Yan, R. W. Lau, · 2020
Closest in time.
Density-aware graph for deep semi-supervised visual recognition,
S. Li, B. Liu, D. Chen, Q. Chu, L. Yuan, N. Yu, · 2020
Closest in time.
Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring,
D. Berthelot, N. Carlini, E. D. Cubuk, A. Kurakin, K. Sohn, H. Zhang, C. Raffel, · 2020
Closest in time.
Randaugment: Practical automated data augmentation with a reduced search space,
E. D. Cubuk, B. Zoph, J. Shlens, Q. V. Le, · 2020
Closest in time.
Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning,
P. Cascante-Bonilla, F. Tan, Y. Qi, V. Ordonez, · 2021
Closest in time.