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Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.
Iterative reclassification procedure for constructing an asymptotically optimal rule of allocation in discriminant analysis
Geoffrey J McLachlan · 1975
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David MacKay John Bridle, Anthony Heading · 1991
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Semi-supervised self-training of object detection models
Chuck Rosenberg, Martial Hebert, and Henry Schneiderman · 2005
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Semi-supervised learning literature survey
Xiaojin Jerry Zhu · 2005
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Semi-Supervised Learning
Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien (eds.) · 2006
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 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 et al · 2009
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Introduction to semi-supervised learning
Xiaojin Zhu and Andrew B Goldberg · 2009
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Discriminative clustering by regularized information maximization
Ryan Gomes Andreas Krause, Pietro Perona · 2010
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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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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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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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Learning with pseudo-ensembles
Philip Bachman, Ouais Alsharif, and Doina Precup · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
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Multi-modal curriculum learning for semi-supervised image classification
Chen Gong, Dacheng Tao, Stephen J Maybank, Wei Liu, Guoliang Kang, and Jie Yang · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Good semi-supervised learning that requires a bad gan
Zihang Dai, Zhilin Yang, Fan Yang, William W Cohen, and Russ R Salakhutdinov · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Self-training avoids using spurious features under domain shift
Yining Chen, Colin Wei, Ananya Kumar, and Tengyu Ma · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning
Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, and Jinwoo Shin · 2020
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Understanding self-training for gradual domain adaptation
Ananya Kumar, Tengyu Ma, and Percy Liang · 2020
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In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning
Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, and Mubarak Shah · 2020
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Temporal ensembling for semi-supervised learning
Laine Samuli and Aila Timo · 2017
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.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Speech-transformer: a no-recurrence sequence-to-sequence model for speech recognition
Linhao Dong, Shuang Xu, and Bo Xu · 2018
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.
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li · 2020
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Rethinking the value of labels for improving class-imbalanced learning
Yuzhe Yang and Zhi Xu · 2020
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Time-consistent self-supervision for semi-supervised learning
Tianyi Zhou, Shengjie Wang, and Jeff Bilmes · 2020
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Cossl: Co-learning of representation and classifier for imbalanced semi-supervised learning
Yue Fan, Dengxin Dai, and Bernt Schiele · 2021
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Abc: Auxiliary balanced classifier for class-imbalanced semi-supervised learning
Hyuck Lee, Seungjae Shin, and Heeyoung Kim · 2021
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Meta pseudo labels
Hieu Pham, Zihang Dai, Qizhe Xie, and Quoc V Le · 2021
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Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning
Chen Wei, Kihyuk Sohn, Clayton Mellina, Alan Yuille, and Fan Yang · 2021
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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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Adamatch: A unified approach to semi-supervised learning and domain adaptation
David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, and Alex Kurakin · 2022
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Class-imbalanced semi-supervised learning with adaptive thresholding
Lan-Zhe Guo and Yu-Feng Li · 2022
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Usb: A unified semi-supervised learning benchmark for classification
Yidong Wang, Hao Chen, Yue Fan, SUN Wang, Ran Tao, Wenxin Hou, Renjie Wang, Linyi Yang, Zhi Zhou, Lan-Zhe Guo, et al · 2022
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