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The recently proposed FixMatch achieved state-of-the-art results on most semi-supervised learning (SSL) benchmarks.
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Curriculum learning of multiple tasks
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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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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 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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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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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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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Curriculum semi-supervised segmentation
Hoel Kervadec, Jose Dolz, Éric Granger, and Ismail Ben Ayed · 2019
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le · 2020
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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
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Automated curriculum learning for neural networks
Alex Graves, Marc G Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu · 2017
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Curriculum learning by transfer learning: Theory and experiments with deep networks
Daphna Weinshall, Gad Cohen, and Dan Amir · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D Cubuk, and Ian J Goodfellow · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Context encoding for semantic segmentation
Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, and Amit Agrawal · 2018
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Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness · 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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Time-consistent self-supervision for semi-supervised learning
Tianyi Zhou, Shengjie Wang, and Jeff Bilmes · 2020
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Multi-task curriculum framework for open-set semi-supervised learning
Qing Yu, Daiki Ikami, Go Irie, and Kiyoharu Aizawa · 2020
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Sentiment analysis via semi-supervised learning: a model based on dynamic threshold and multi-classifiers
Yue Han, Yuhong Liu, and Zhigang Jin · 2020
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fixmatch
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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Fixmatch-pytorch
Lee Doyup and Cheon Yeongjae · 2020
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Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, and Mubarak Shah · 2021
Closest in time.
Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation
Zhedong Zheng and Yi Yang · 2021
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Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning
Paola Cascante-Bonilla, Fuwen Tan, Yanjun Qi, and Vicente Ordonez · 2021
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