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The goal of semi-supervised learning is to utilize the unlabeled, in-domain dataset U to improve models trained on the labeled dataset D.
Decoupled certainty-driven consistency loss for semi-supervised learning
Yiting Li, Lu Liu, and Robby T Tan. 2019b · 1901
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le. 2019 · 1904
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Augmenting data with mixup for sentence classification: An empirical study
Hongyu Guo, Yongyi Mao, and Richong Zhang. 2019 · 1905
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Billion-scale semi-supervised learning for image classification
I Zeki Yalniz, Hervé Jégou, Kan Chen, Manohar Paluri, and Dhruv Mahajan. 2019 · 1905
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Revisiting self-training for neural sequence generation
Junxian He, Jiatao Gu, Jiajun Shen, and Marc’Aurelio Ranzato. 2019 · 1909
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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Probability of error of some adaptive pattern-recognition machines
H. Scudder. 1965 · 1965
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Semi-supervised learning for neural machine translation
Yong Cheng, Wei Xu, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2016 · 1974
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Learning extraction patterns for subjective expressions
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2005
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Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio. 2005 · 2005
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Improved noisy student training for automatic speech recognition
Daniel S Park, Yu Zhang, Ye Jia, Wei Han, Chung-Cheng Chiu, Bo Li, Yonghui Wu, and Quoc V Le. 2020 · 2005
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Semi-supervised learning literature survey
Xiaojin Jerry Zhu. 2005 · 2005
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Semi-Supervised Learning
Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien. 2006 · 2006
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2006
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Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al. 2020 · 2006
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Semi-supervised text classification using em
Kamal Nigam, Andrew McCallum, and Tom M Mitchell. 2006 · 2006
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D Cubuk, and Quoc V Le. 2020 · 2006
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A simple semi-supervised algorithm for named entity recognition
Wenhui Liao and Sriharsha Veeramachaneni. 2009 · 2009
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Learning word vectors for sentiment analysis
Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. 2011 · 2011
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Finding deceptive opinion spam by any stretch of the imagination
Myle Ott, Yejin Choi, Claire Cardie, and Jeffrey T. Hancock. 2011 · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee. 2013 · 2013
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Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Neural word embedding as implicit matrix factorization
Omer Levy and Yoav Goldberg. 2014 · 2014
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Towards a general rule for identifying deceptive opinion spam
Jiwei Li, Myle Ott, Claire Cardie, and Eduard Hovy. 2014 · 2014
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Transformation pursuit for image classification
Mattis Paulin, Jérôme Revaud, Zaid Harchaoui, Florent Perronnin, and Cordelia Schmid. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
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Semi-supervised sequence learning
Andrew M Dai and Quoc V Le. 2015 · 2015
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Semi-supervised learning with ladder networks
Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund, and Tapani Raiko. 2015 · 2015
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Training deep neural networks on noisy labels with bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich. 2015 · 2015
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That’s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using #petpeeve tweets
William Yang Wang and Diyi Yang. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Transductive semi-supervised deep learning using min-max features
Weiwei Shi, Yihong Gong, Chris Ding, Zhiheng MaXiaoyu Tao, and Nanning Zheng. 2018 · 2018
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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. 2018 · 2018
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Noising and denoising natural language: Diverse backtranslation for grammar correction
Ziang Xie, Guillaume Genthial, Stanley Xie, Andrew Y Ng, and Dan Jurafsky. 2018 · 2018
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Not enough data? deep learning to the rescue!
Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, Naama Tepper, and Naama Zwerdling. 2019 · 2019
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There are many consistent explanations of unlabeled data: Why you should average
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson. 2019 · 2019
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Samuli Laine and Timo Aila. 2016 · 2016
Cited alongside, same era.
Adversarial training methods for semi-supervised text classification
Takeru Miyato, Andrew M Dai, and Ian Goodfellow. 2016 · 2016
Cited alongside, same era.
Unsupervised pretraining for sequence to sequence learning
Prajit Ramachandran, Peter J Liu, and Quoc V Le. 2016 · 2016
Cited alongside, same era.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen. 2016 · 2016
Cited alongside, same era.
Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Effective LSTMs for target-dependent sentiment classification
Duyu Tang, Bing Qin, Xiaocheng Feng, and Ting Liu. 2016 · 2016
Cited alongside, same era.
Neural machine translation with reconstruction
Zhaopeng Tu, Yang Liu, Lifeng Shang, Xiaohua Liu, and Hang Li. 2016 · 2016
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Semi-supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks
Yauhen Babakhin, Artsiom Sanakoyeu, and Hirotoshi Kitamura. 2019 · 2019
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Soft contextual data augmentation for neural machine translation
Fei Gao, Jinhua Zhu, Lijun Wu, Yingce Xia, Tao Qin, Xueqi Cheng, Wengang Zhou, and Tie-Yan Liu. 2019 · 2019
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Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum. 2019 · 2019
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Leveraging just a few keywords for fine-grained aspect detection through weakly supervised co-training
Giannis Karamanolakis, Daniel Hsu, and Luis Gravano. 2019 · 2019
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Lessons from building acoustic models with a million hours of speech
Sree Hari Krishnan Parthasarathi and Nikko Strom. 2019 · 2019
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Semi-supervised neural text generation by joint learning of natural language generation and natural language understanding models
Raheel Qader, François Portet, and Cyril Labbé. 2019 · 2019
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Semi-supervised text style transfer: Cross projection in latent space
Mingyue Shang, Piji Li, Zhenxin Fu, Lidong Bing, Dongyan Zhao, Shuming Shi, and Rui Yan. 2019 · 2019
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Low resource text classification with ulmfit and backtranslation
Sam Shleifer. 2019 · 2019
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Xlda: Cross-lingual data augmentation for natural language inference and question answering
Jasdeep Singh, Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2019 · 2019
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Interpolation consistency training for semi-supervised learning
Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, and David Lopez-Paz. 2019 · 2019
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Eda: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019 · 2019
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Conditional bert contextual augmentation
Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han, and Songlin Hu. 2019 · 2019
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Generalized data augmentation for low-resource translation
Mengzhou Xia, Xiang Kong, Antonios Anastasopoulos, and Graham Neubig. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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A semi-supervised approach for low-resourced text generation
Hongyu Zang and Xiaojun Wan. 2019 · 2019
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Pseudo-labeling and confirmation bias in deep semi-supervised learning
Eric Arazo, Diego Ortego, Paul Albert, Noel E O’Connor, and Kevin McGuinness. 2020 · 2020
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Unilmv2: Pseudo-masked language models for unified language model pre-training
Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Xiaodong Liu, Yu Wang, Songhao Piao, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2020 · 2020
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Description based text classification with reinforcement learning
Duo Chai, Wei Wu, Qinghong Han, Fei Wu, and Jiwei Li. 2020 · 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 · 2020
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
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Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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Data augmentation using pre-trained transformer models
Varun Kumar, Ashutosh Choudhary, and Eunah Cho. 2020 · 2020
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Decoupled certainty-driven consistency loss for semi-supervised learning
Lu Liu, Yiting Li, and Robby T. Tan. 2020 · 2020
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le. 2020 · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le. 2020 · 2020
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