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Pseudo-labeling (PL) and Data Augmentation-based Consistency Training (DACT) are two approaches widely used in Semi-Supervised Learning (SSL) methods.
Transfusion: Understanding transfer learning for medical imaging
Raghu, M.; Zhang, C.; Kleinberg, J.; and Bengio, S. 2019 · 1902
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A critical analysis of self-supervision, or what we can learn from a single image
Asano, Y. M.; Rupprecht, C.; and Vedaldi, A. 2019 · 1904
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Lim, S.; Kim, I.; Kim, T.; Kim, C.; and Kim, S. 2019 · 1905
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Stada: Style transfer as data augmentation
Zheng, X.; Chalasani, T.; Ghosal, K.; Lutz, S.; and Smolic, A. 2019 · 1909
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Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring
Berthelot, D.; Carlini, N.; Cubuk, E. D.; Kurakin, A.; Sohn, K.; Zhang, H.; and Raffel, C. 2019a · 1911
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Big transfer (bit): General visual representation learning
Kolesnikov, A.; Beyer, L.; Zhai, X.; Puigcerver, J.; Yung, J.; Gelly, S.; and Houlsby, N. 2019 · 1912
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Meta-weight-net: Learning an explicit mapping for sample weighting
Shu, J.; Xie, Q.; Yi, L.; Zhao, Q.; Zhou, S.; Xu, Z.; and Meng, D. 2019 · 1930
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Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning
Cascante-Bonilla, P.; Tan, F.; Qi, Y.; and Ordonez, V. 2020 · 2001
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Sohn, K.; Berthelot, D.; Li, C.-L.; Zhang, Z.; Carlini, N.; Cubuk, E. D.; Kurakin, A.; Zhang, H.; and Raffel, C. 2020 · 2001
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A simple framework for contrastive learning of visual representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020a · 2002
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Learning from labeled and unlabeled data with label propagation
Zhu, X.; and Ghahramani, Z. 2002 · 2002
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Improved baselines with momentum contrastive learning
Chen, X.; Fan, H.; Girshick, R.; and He, K. 2020c · 2003
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Pham, H.; Xie, Q.; Dai, Z.; and Le, Q. V. 2020 · 2003
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Semi-supervised learning by entropy minimization
Grandvalet, Y.; and Bengio, Y. 2005 · 2005
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What makes for good views for contrastive learning
Tian, Y.; Sun, C.; Poole, B.; Krishnan, D.; Schmid, C.; and Isola, P. 2020 · 2005
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Bootstrap your own latent: A new approach to self-supervised learning
Grill, J.-B.; Strub, F.; Altché, F.; Tallec, C.; Richemond, P. H.; Buchatskaya, E.; Doersch, C.; Pires, B. A.; Guo, Z. D.; Azar, M. G.; et al. 2020 · 2006
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Demystifying self-supervised learning: An information-theoretical framework
Tsai, Y.-H. H.; Wu, Y.; Salakhutdinov, R.; and Morency, L.-P. 2020 · 2006
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What makes instance discrimination good for transfer learning?
Zhao, N.; Wu, Z.; Lau, R. W.; and Lin, S. 2020a · 2006
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A kernel method for the two-sample-problem
Gretton, A.; Borgwardt, K.; Rasch, M.; Schölkopf, B.; and Smola, A. J. 2007 · 2007
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Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning
Kim, J.; Hur, Y.; Park, S.; Yang, E.; Hwang, S. J.; and Shin, J. 2020 · 2007
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Featmatch: Feature-based augmentation for semi-supervised learning
Kuo, C.-W.; Ma, C.-Y.; Huang, J.-B.; and Kira, Z. 2020 · 2007
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What should not be contrastive in contrastive learning
Xiao, T.; Wang, X.; Efros, A. A.; and Darrell, T. 2020 · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
Cheng, H.; Zhu, Z.; Li, X.; Gong, Y.; Sun, X.; and Liu, Y. 2020 · 2010
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Robust Semi-Supervised Learning with Out of Distribution Data
Zhao, X.; Krishnateja, K.; Iyer, R.; and Chen, F. 2020b · 2010
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Exploring Simple Siamese Representation Learning
Chen, X.; and He, K. 2020 · 2011
Cited alongside, same era.
Reading digits in natural images with unsupervised feature learning
Netzer, Y.; Wang, T.; Coates, A.; Bissacco, A.; Wu, B.; and Ng, A. Y. 2011 · 2011
Cited alongside, same era.
Clusterability as an alternative to anchor points when learning with noisy labels
Zhu, Z.; Song, Y.; and Liu, Y. 2021 · 2011
Cited alongside, same era.
VideoMix: Rethinking Data Augmentation for Video Classification
Yun, S.; Oh, S. J.; Heo, B.; Han, D.; and Kim, J. 2020 · 2012
Cited alongside, same era.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H. 2013 · 2013
Cited alongside, same era.
Representation learning with contrastive predictive coding
Oord, A. v. d.; Li, Y.; and Vinyals, O. 2018 · 2018
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Deep visual domain adaptation: A survey
Wang, M.; and Deng, W. 2018 · 2018
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Learning a deep convnet for multi-label classification with partial labels
Durand, T.; Mehrasa, N.; and Mori, G. 2019 · 2019
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Label propagation for deep semi-supervised learning
Iscen, A.; Tolias, G.; Avrithis, Y.; and Chum, O. 2019 · 2019
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Style augmentation: data augmentation via style randomization
Jackson, P. T.; Abarghouei, A. A.; Bonner, S.; Breckon, T. P.; and Obara, B. 2019 · 2019
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A survey on image data augmentation for deep learning
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Transfer feature learning with joint distribution adaptation
Long, M.; Wang, J.; Ding, G.; Sun, J.; and Yu, P. S. 2013 · 2013
Cited alongside, same era.
Learning with noisy labels
Natarajan, N.; Dhillon, I. S.; Ravikumar, P.; and Tewari, A. 2013 · 2013
Cited alongside, same era.
On handling negative transfer and imbalanced distributions in multiple source transfer learning
Ge, L.; Gao, J.; Ngo, H.; Li, K.; and Zhang, A. 2014 · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
Cited alongside, same era.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F.; Seff, A.; Zhang, Y.; Song, S.; Funkhouser, T.; and Xiao, J. 2015 · 2015
Cited alongside, same era.
Towards Deep Style Transfer: A Content-Aware Perspective
Chen, Y.-L.; and Hsu, C.-T. 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Shorten, C.; and Khoshgoftaar, T. M. 2019 · 2019
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Characterizing and avoiding negative transfer
Wang, Z.; Dai, Z.; Póczos, B.; and Carbonell, J. 2019 · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Caron, M.; Misra, I.; Mairal, J.; Goyal, P.; Bojanowski, P.; and Joulin, A. 2020 · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D.; Zoph, B.; Shlens, J.; and Le, Q. V. 2020 · 2020
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Learning with multiple complementary labels
Feng, L.; Kaneko, T.; Han, B.; Niu, G.; An, B.; and Sugiyama, M. 2020 · 2020
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Safe deep semi-supervised learning for unseen-class unlabeled data
Guo, L.-Z.; Zhang, Z.-Y.; Jiang, Y.; Li, Y.-F.; and Zhou, Z.-H. 2020 · 2020
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Momentum contrast for unsupervised visual representation learning
He, K.; Fan, H.; Wu, Y.; Xie, S.; and Girshick, R. 2020 · 2020
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OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data
Jongjin, P.; Sukmin, Y.; Jeong, J.; and Shin, J. 2020 · 2020
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Density-Aware Graph for Deep Semi-Supervised Visual Recognition
Li, S.; Liu, B.; Chen, D.; Chu, Q.; Yuan, L.; and Yu, N. 2020 · 2020
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Tradeoffs in Data Augmentation: An Empirical Study
Raphael, G.; Sylvia, S.; Ekin, D.; and D., E. 2020 · 2020
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Repetitive reprediction deep decipher for semi-supervised learning
Wang, G.-H.; and Wu, J. 2020 · 2020
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On the generalization effects of linear transformations in data augmentation
Wu, S.; Zhang, H.; Valiant, G.; and Ré, C. 2020 · 2020
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Contrastive Learning with Stronger Augmentations
Xiao, W.; and Guo-Jun, Q. 2020 · 2020
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Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
Yu, Q.; Ikami, D.; Irie, G.; and Aizawa, K. 2020 · 2020
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On Data-Augmentation and Consistency-Based Semi-Supervised Learning
Ghosh, A.; and Thiery, A. H. 2021 · 2021
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Rizve, M. N.; Duarte, K.; Rawat, Y. S.; and Shah, M. 2021 · 2021
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Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis
Teng, J.; and Huang, W. 2021 · 2021
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Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Zbontar, J.; Jing, L.; Misra, I.; LeCun, Y.; and Deny, S. 2021 · 2021
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Domain generalization with mixstyle
Zhou, K.; Yang, Y.; Qiao, Y.; and Xiang, T. 2021 · 2021
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A second-order approach to learning with instance-dependent label noise
Zhu, Z.; Liu, T.; and Liu, Y. 2021 · 2021
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