Fetching the paper…
Reading the bibliography…
While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelled and unlabelled data, they generally struggle when the number of annotated samples is very small.
A method for solving the convex programming problem with convergence rate o (1/k
Y. Nesterov · 1983
Earlier work this paper cites.
Combining labeled and unlabeled data with co-training
A. Blum and T. Mitchell · 1998
Earlier work this paper cites.
Learning from labeled and unlabeled data with label propagation
X. Zhu and Z. Ghahramani · 2002
Earlier work this paper cites.
Tri-training: Exploiting unlabeled data using three classifiers
Z.-H. Zhou and M. Li · 2005
Earlier work this paper cites.
Semi-Supervised Learning
O. Chapelle and A. Scholkopf, B.and Zien · 2006
Earlier work this paper cites.
Semi-supervised learning in gigantic image collections
R. Fergus, Y. Weiss, and A. Torralba · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Recognizing indoor scenes
A. Quattoni and Antonio Torralba · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Y Netzer et al · 2011
Earlier work this paper cites.
Semi-supervised learning improves gene expression-based prediction of cancer recurrence
M. Shi and B. Zhang · 2011
Earlier work this paper cites.
Tracking-based semi-supervised learning
A. Teichman and S.n Thrun · 2012
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
D.-H. Lee · 2013
Earlier work this paper cites.
Learning with pseudo-ensembles
P. Bachman, O. Alsharif, and D. Precup · 2014
Earlier work this paper cites.
Multi-fold mil training for weakly supervised object localization
Ramazan Gokberk Cinbis, Jakob Verbeek, and Cordelia Schmid · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava et al · 2014
Cited alongside, same era.
Learning deep features for scene recognition using places database
B. Zhou et al · 2014
Cited alongside, same era.
Semi-supervised learning with ladder networks
A. Rasmus et al · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky et al · 2015
Cited alongside, same era.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
J. Springenberg · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He et al · 2016
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
S. Gidaris, P. Singh, and N. Komodakis · 2018
Later among the works it cites.
Semi-supervised learning via compact latent space clustering
K. Kamnitsas et al · 2018
Later among the works it cites.
Smooth neighbors on teacher graphs for semi-supervised learning
Y. Luo, J. Zhu, et al · 2018
Later among the works it cites.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
T. Miyato et al · 2018
Later among the works it cites.
Realistic evaluation of deep semi-supervised learning algorithms
A. Oliver et al · 2018
Later among the works it cites.
When semi-supervised learning meets transfer learning: Training strategies, models and datasets
H. Zhou et al · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
M. Sajjadi, M. Javanmardi, and T. Tasdizen · 2016
Cited alongside, same era.
Improved techniques for training gans
T. Salimans et al · 2016
Cited alongside, same era.
The reversible residual network: Backpropagation without storing activations
A. N. Gomez et al · 2017
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
S. Laine and T. Aila · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
S.-A. Rebuffi et al · 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.
Later among the works it cites.
Semi-supervised learning by label gradient alignment
J. Jackson, J.and Schulman · 2019
Closest in time.
Revisiting self-supervised visual representation learning
X. Kolesnikov, A. andZhai and L Beyer · 2019
Closest in time.
Generalized label propagation methods for semi-supervised learning
Q. Li, X-M. Wu, and Z. Guan · 2019
Closest in time.
Certainty-driven consistency loss for semi-supervised learning
Y. Li, L. Liu, and R. Tan · 2019
Closest in time.
Interpolation consistency training for semi-supervised learning
Verma.V et al · 2019
Closest in time.
Aet vs. aed: Unsupervised representation learning by auto-encoding transformations rather than data
L. Zhang et al · 2019
Closest in time.
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
Closest in time.