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Semi-supervised learning (SSL) is an efficient framework that can train models with both labeled and unlabeled data, but may generate ambiguous and non-distinguishable representations when lacking adequate labeled samples.
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L. Van der Maaten and G. Hinton, · 2008
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“Multi-class active learning for image classification,”
A. J. Joshi, F. Porikli, and N. Papanikolopoulos, · 2009
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“Learning multiple layers of features from tiny images,”
A. Krizhevsky, · 2009
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“Reading digits in natural images with unsupervised feature learning,”
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, · 2011
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“Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks,”
D. Lee, · 2013
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“Active learning: A survey,”
C. Aggarwal, X. Kong, Q. Gu, J. Han, and P. Yu, · 2014
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“Large-scale live active learning: Training object detectors with crawled data and crowds,”
S. Vijayanarasimhan and K. Grauman, · 2014
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“Fast r-cnn,”
R. Girshick, · 2015
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“Regularization with stochastic transformations and perturbations for deep semi-supervised learning,”
M. Sajjadi, M. Javanmardi, and T. Tasdizen, · 2016
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“Deep residual learning for image recognition,”
K. He, X. Zhang, S. Ren, and J Sun, · 2016
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“Mask r-cnn,”
K. He, G. Gkioxari, P. Dollár, and R. Girshick, · 2017
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“Temporal ensembling for semi-supervised learning,”
S. Laine and T. Aila, · 2017
Cited alongside, same era.
“Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,”
A. Tarvainen and H. Valpola, · 2017
Cited alongside, same era.
“Improved regularization of convolutional neural networks with cutout,”
T. Devries and G. W. Taylor, · 2017
Cited alongside, same era.
“SGDR: stochastic gradient descent with warm restarts,”
I. Loshchilov and F. Hutter, · 2017
Cited alongside, same era.
“Residual networks of residual networks: Multilevel residual networks,”
K. Zhang, M. Sun, T. Han, X. Yuan, L. Guo, and T. Liu, · 2018
Cited alongside, same era.
“Exploring the limits of weakly supervised pretraining,”
“Unsupervised data augmentation for consistency training,”
Q. Xie, Z. Dai, E. H. Hovy, T. Luong, and Q. Le, · 2020
Later among the works it cites.
“Fixmatch: Simplifying semi-supervised learning with consistency and confidence,”
K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. Raffel, E. D. Cubuk, A. Kurakin, and C. Li, · 2020
Later among the works it cites.
“A simple framework for contrastive learning of visual representations,”
T. Chen, S. Kornblith, M. Norouzi, and G. E. Hinton, · 2020
Later among the works it cites.
“Momentum contrast for unsupervised visual representation learning,”
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, · 2020
Later among the works it cites.
“Big self-supervised models are strong semi-supervised learners,”
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. E. Hinton, · 2020
Later among the works it cites.
“CSI: novelty detection via contrastive learning on distributionally shifted instances,”
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D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten, · 2018
Cited alongside, same era.
“Mixmatch: A holistic approach to semi-supervised learning,”
D. Berthelot, N. Carlini, I. J. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, · 2019
Cited alongside, same era.
“Learning representations by maximizing mutual information across views,”
P. Bachman, R. D. Hjelm, and W. Buchwalter, · 2019
Cited alongside, same era.
“Uncertainty-based active learning via sparse modeling for image classification,”
G. Wang, J. Hwang, C. Rose, and F. Wallace, · 2019
Cited alongside, same era.
“Self-training with noisy student improves imagenet classification,”
Q. Xie, M.-T. Luong, E. H. Hovy, and Q. V. Le, · 2020
Cited alongside, same era.
“Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring,”
D. Berthelot, N. Carlini, E. D. Cubuk, A. Kurakin, K. Sohn, H. Zhang, and C. Raffel, · 2020
Cited alongside, same era.
J. Tack, S. Mo, J. Jeong, and J. Shin, · 2020
Later among the works it cites.
“Supervised contrastive learning,”
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, · 2020
Later among the works it cites.
“Consistency-based semi-supervised active learning: Towards minimizing labeling cost,”
M. Gao, Z. Zhang, G. Yu, S. Ö. Arik, L. S. Davis, and T. Pfister, · 2020
Later among the works it cites.
“Randaugment: Practical automated data augmentation with a reduced search space,”
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, · 2020
Later among the works it cites.
“Selfmatch: Combining contrastive self-supervision and consistency for semi-supervised learning,”
B. Kim, J. Choo, Y. Kwon, S. Joe, S. Min, and Y. Gwon, · 2021
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