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We present label gradient alignment, a novel algorithm for semi-supervised learning which imputes labels for the unlabeled data and trains on the imputed labels.
The truncatedsvd as a method for regularization
Hansen, P. C · 1987
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Learning from labeled and unlabeled data with label propagation
Zhu, X. and Ghahramani, Z · 2002
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Semi-supervised learning by entropy minimization
Grandvalet, Y. and Bengio, Y · 2005
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Semi-Supervised Learning
Chapelle, O., Schlkopf, B., and Zien, A · 2010
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Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Semi-supervised learning with ladder networks
Rasmus, A., Berglund, M., Honkala, M., Valpola, H., and Raiko, T · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Temporal ensembling for semi-supervised learning
Laine, S. and Aila, T · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Sajjadi, M., Javanmardi, M., and Tasdizen, T · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
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Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A. and Valpola, H · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Gradient similarity: An explainable approach to detect adversarial attacks against deep learning
Dhaliwal, J. and Shintre, S · 2018
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Adapting auxiliary losses using gradient similarity
Du, Y., Czarnecki, W. M., Jayakumar, S. M., Pascanu, R., and Lakshminarayanan, B · 2018
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Semi-supervised learning via compact latent space clustering
Kamnitsas, K., Castro, D., Folgoc, L. L., Walker, I., Tanno, R., Rueckert, D., Glocker, B., Criminisi, A., and Nori, A · 2018
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Cifar-10 (canadian institute for advanced research)
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
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Realistic evaluation of deep semi-supervised learning algorithms
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Manifold mixup: Encouraging meaningful on-manifold interpolation as a regularizer
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There are many consistent explanations of unlabeled data: Why you should average
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