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Semi-supervised learning has emerged as a pivotal approach for leveraging scarce labeled data alongside abundant unlabeled data.
Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring
David Berthelot, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 1911
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Expectations and entropy inequalities for finite quantum systems
Göran Lindblad · 1974
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Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky · 1995
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
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Functions of matrices: theory and computation
Nicholas J Higham · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Lie groups, Lie algebras, and representations
Brian C Hall · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
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Information geometry of positive measures and positive-definite matrices: Decomposable dually flat structure
Shun-ichi Amari · 2014
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
Cited alongside, same era.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
Cited alongside, same era.
Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 2019
Cited alongside, same era.
When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Alphamatch: Improving consistency for semi-supervised learning with alpha-divergence
Chengyue Gong, Dilin Wang, and Qiang Liu · 2021
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Simple: similar pseudo label exploitation for semi-supervised classification
Zijian Hu, Zhengyu Yang, Xuefeng Hu, and Ram Nevatia · 2021
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Comatch: Semi-supervised learning with contrastive graph regularization
Junnan Li, Caiming Xiong, and Steven CH Hoi · 2021
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All labels are not created equal: Enhancing semi-supervision via label grouping and co-training
Islam Nassar, Samitha Herath, Ehsan Abbasnejad, Wray Buntine, and Gholamreza Haffari · 2021
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Meta pseudo labels
Hieu Pham, Zihang Dai, Qizhe Xie, and Quoc V Le · 2021
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Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
On mutual information maximization for representation learning
Michael Tschannen, Josip Djolonga, Paul K Rubenstein, Sylvain Gelly, and Mario Lucic · 2019
Cited alongside, same era.
Simmatch: Semi-supervised learning with similarity matching
Mingkai Zheng, Shan You, Lang Huang, Fei Wang, Chen Qian, and Chang Xu · 2019
Cited alongside, same era.
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li · 2020
Cited alongside, same era.
Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2020
Cited alongside, same era.
Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le · 2020
Cited alongside, same era.
Semi-supervised learning of visual features by non-parametrically predicting view assignments with support samples
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Armand Joulin, Nicolas Ballas, and Michael Rabbat · 2021
Cited alongside, same era.
Xudong Wang, Long Lian, and Stella X Yu · 2021
Later among the works it cites.
Dash: Semi-supervised learning with dynamic thresholding
Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, and Rong Jin · 2021
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Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling
Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, and Takahiro Shinozaki · 2021
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Randall Balestriero and Yann LeCun · 2022
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Maxmatch: Semi-supervised learning with worst-case consistency
Yangbangyan Jiang Xiaodan Li, Yuefeng Chen, Yuan He, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, and Qingming Huang · 2022
Later among the works it cites.
Softmatch: Addressing the quantity-quality trade-off in semi-supervised learning
Hao Chen, Ran Tao, Yue Fan, Yidong Wang, Jindong Wang, Bernt Schiele, Xing Xie, Bhiksha Raj, and Marios Savvides · 2023
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Seal: Simultaneous label hierarchy exploration and learning
Zhiquan Tan, Zihao Wang, and Yifan Zhang · 2023
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