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Deep neural networks achieve remarkable performances on a wide range of tasks with the aid of large-scale labeled datasets.
Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky · 1995
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Combining labeled and unlabeled data with co-training
Avrim Blum and Tom Mitchell · 1998
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, R. Fergus, and P. Perona · 2004
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Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
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Semi-supervised self-training of object detection models
Chuck Rosenberg, Martial Hebert, and Henry Schneiderman · 2005
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 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
Earlier work this paper cites.
Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
Earlier work this paper cites.
Collecting a large-scale dataset of fine-grained cars
Jonathan Krause, Jia Deng, Michael Stark, and Li Fei-Fei · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
Earlier work this paper cites.
Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
Earlier work this paper cites.
Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Semi-supervised learning with generative adversarial networks
Augustus Odena · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 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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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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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Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification
Yixiao Ge, Dapeng Chen, and Hongsheng Li · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Dividemix: Learning with noisy labels as semi-supervised learning
Junnan Li, Richard Socher, and Steven CH Hoi · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 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
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.
Adversarial dropout for supervised and semi-supervised learning
Sungrae Park, JunKeon Park, Su-Jin Shin, and Il-Chul Moon · 2018
Cited alongside, same era.
Strong baselines for neural semi-supervised learning under domain shift
Sebastian Ruder and Barbara Plank · 2018
Cited alongside, same era.
Transductive semi-supervised deep learning using min-max features
Weiwei Shi, Yihong Gong, Chris Ding, Zhiheng MaXiaoyu Tao, and Nanning Zheng · 2018
Cited alongside, same era.
Deep mutual learning
Ying Zhang, Tao Xiang, Timothy M Hospedales, and Huchuan Lu · 2018
Cited alongside, same era.
Teppei Suzuki and Ikuro Sato · 2020
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le · 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
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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
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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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Meta pseudo labels
Hieu Pham, Zihang Dai, Qizhe Xie, and Quoc V Le · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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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 · 2021
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A realistic evaluation of semi-supervised learning for fine-grained classification
Jong-Chyi Su, Zezhou Cheng, and Subhransu Maji · 2021
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Self-tuning for data-efficient deep learning
Ximei Wang, Jinghan Gao, Mingsheng Long, and Jianmin Wang · 2021
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Theoretical analysis of self-training with deep networks on unlabeled data
Colin Wei, Kendrick Shen, Yining Chen, and Tengyu Ma · 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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What makes instance discrimination good for transfer learning?
Nanxuan Zhao, Zhirong Wu, Rynson W. H. Lau, and Stephen Lin · 2021
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Transferability in deep learning: A survey, 2022
Junguang Jiang, Yang Shu, Jianmin Wang, and Mingsheng Long · 2022
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Daso: Distribution-aware semantics-oriented pseudo-label for imbalanced semi-supervised learning
Youngtaek Oh, Dong-Jin Kim, and In So Kweon · 2022
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Debiased learning from naturally imbalanced pseudo-labels for zero-shot and semi-supervised learning
Xudong Wang, Zhirong Wu, Long Lian, and Stella X Yu · 2022
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