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Semi-Supervised Learning (SSL) has achieved great success in overcoming the difficulties of labeling and making full use of unlabeled data.
The advanced theory of statistics
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Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]
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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
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Learning multiple layers of features from tiny images
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Semi-supervised learning for imbalanced sentiment classification
Li, S., Wang, Z., Zhou, G., and Lee, S. Y. M · 2011
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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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Learning with pseudo-ensembles
Bachman, P., Alsharif, O., and Precup, D · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Semi-supervised self-training approaches for imbalanced splice site datasets
Stanescu, A. and Caragea, D · 2014
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The impact of imbalanced training data for convolutional neural networks, 2015
Masko, D. and Hensman, P · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
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Best of both worlds: human-machine collaboration for object annotation
Russakovsky, O., Li, L.-J., and Fei-Fei, L · 2015
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What’s the point: Semantic segmentation with point supervision
Bearman, A., Russakovsky, O., Ferrari, V., and Fei-Fei, L · 2016
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Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., and Courville, A · 2016
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Learning deep representation for imbalanced classification
Huang, C., Li, Y., Change Loy, C., and Tang, X · 2016
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Learning from imbalanced data: open challenges and future directions
Krawczyk, B · 2016
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Temporal ensembling for semi-supervised learning
Laine, S. and Aila, T · 2016
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Plankton classification on imbalanced large scale database via convolutional neural networks with transfer learning
Lee, H., Park, M., and Kim, J · 2016
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Ssd: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2016
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A systematic study of the class imbalance problem in convolutional neural networks
Buda, M., Maki, A., and Mazurowski, M. A · 2018
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Imbalanced deep learning by minority class incremental rectification
Dong, Q., Gong, S., and Zhu, X · 2018
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Manifold regularization with gans for semi-supervised learning
Lecouat, B., Foo, C.-S., Zenati, H., and Chandrasekhar, V · 2018
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Smooth neighbors on teacher graphs for semi-supervised learning
Luo, Y., Zhu, J., Li, M., Ren, Y., and Zhang, B · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S.-i., Koyama, M., and Ishii, S · 2018
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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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Training deep neural networks on imbalanced data sets
Wang, S., Liu, W., Wu, J., Cao, L., Meng, Q., and Kennedy, P. J · 2016
Cited alongside, same era.
Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
Training cost-sensitive deep belief networks on imbalance data problems
Zhang, C., Tan, K. C., and Ren, R · 2016
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Deep over-sampling framework for classifying imbalanced data
Ando, S. and Huang, C. Y · 2017
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Cost-sensitive learning of deep feature representations from imbalanced data
Khan, S. H., Hayat, M., Bennamoun, M., Sohel, F. A., and Togneri, R · 2017
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Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
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Realistic evaluation of deep semi-supervised learning algorithms
Oliver, A., Odena, A., Raffel, C. A., Cubuk, E. D., and Goodfellow, I · 2018
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Dynamic sampling in convolutional neural networks for imbalanced data classification
Pouyanfar, S., Tao, Y., Mohan, A., Tian, H., Kaseb, A. S., Gauen, K., Dailey, R., Aghajanzadeh, S., Lu, Y.-H., Chen, S.-C., et al · 2018
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Predicting hospital readmission via cost-sensitive deep learning
Wang, H., Cui, Z., Chen, Y., Avidan, M., Abdallah, A. B., and Kronzer, A · 2018
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Mixmatch: A holistic approach to semi-supervised learning
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., and Raffel, C · 2019
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Learning imbalanced datasets with label-distribution-aware margin loss
Cao, K., Wei, C., Gaidon, A., Arechiga, N., and Ma, T · 2019
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Class-balanced loss based on effective number of samples
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., and Belongie, S · 2019
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Consistency-based semi-supervised learning for object detection
Jeong, J., Lee, S., Kim, J., and Kwak, N · 2019
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Survey on deep learning with class imbalance
Johnson, J. M. and Khoshgoftaar, T. M · 2019
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Interpolation consistency training for semi-supervised learning
Verma, V., Lamb, A., Kannala, J., Bengio, Y., and Lopez-Paz, D · 2019
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S4l: Self-supervised semi-supervised learning
Zhai, X., Oliver, A., Kolesnikov, A., and Beyer, L · 2019
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