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Semi-supervised learning, i.e.
Y. Grandvalet and Y. Bengio, “Semi-supervised Learning by Entropy Minimization,” in International Conference on Neural Information Processing Systems (NIPS) , 2004
2004
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A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” University of Toronto, Tech. Rep., 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2009
2009
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Y. Netzer, T. Wang, A. Coates, A. Bissacco, . Wu, B, and A. Ng, “Reading digits in natural images with unsupervised feature learning,” in Advances in Neural Information Processing Systems (NeurIPS) , 2011
2011
Earlier work this paper cites.
I. S. G. H. A. Krizhevsky, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems (NeurIPS) , 2012
2012
Earlier work this paper cites.
D. Lee, “Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,” in International Conference on Machine Learning Workshops (ICMLW) , 2013
2013
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” Journal of Machine Learning Research , vol. 15, pp. 1929–1958, 2014
2014
Earlier work this paper cites.
Z. Zhang, F. Ringeval, B. Dong, E. Coutinho, E. Marchi, and B. Schüller, “Enhanced semi-supervised learning for multimodal emotion recognition,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2016
2016
Earlier work this paper cites.
T. Miyato, A. Dai, and I. Goodfellow, “Adversarial Training Methods for Semi-Supervised Text Classification,” arXiv: 1605.07725 , 2016
2016
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M. Sajjadi, M. Javanmardi, and T. Tasdizen, “Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning,” in Advances in Neural Information Processing Systems (NeurIPS) , 2016
2016
Earlier work this paper cites.
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra, “Matching Networks for One Shot Learning,” in Advances in Neural Information Processing Systems (NeurIPS) , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016
2016
Earlier work this paper cites.
T. Salimans and D. Kingma, “Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks,” in Advances in Neural Information Processing Systems (NeurIPS) , 2016
2016
Earlier work this paper cites.
L. Xie, J. Wang, Z. Wei, M. Wang, and Q. Tian, “DisturbLabel: Regularizing CNN on the Loss Layer,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016
2016
Earlier work this paper cites.
N. K. S. Zagoruyko, “Wide Residual Networks,” in British Machine Vision Conference (BMVC) , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity Mappings in Deep Residual Networks,” in European Conference on Computer Vision (ECCV) , 2016
2016
Earlier work this paper cites.
T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, “Focal Loss for Dense Object Detection,” in IEEE International Conference on Computer Vision (ICCV) , 2017
2017
Cited alongside, same era.
W. Li, L. Wang, W. Li, E. Agustsson, and L. Van Gool, “WebVision Database: Visual Learning and Understanding from Web Data,” arXiv: 1708.02862 , 2017
2017
Cited alongside, same era.
A. Tarvainen and H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,” in Advances in Neural Information Processing Systems (NeurIPS) , 2017
2017
Cited alongside, same era.
S. Laine and T. Aila, “Temporal Ensembling for Semi-Supervised Learning,” in International Conference on Learning Representations (ICLR) , 2017
2017
Cited alongside, same era.
S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” in International Conference on Learning Representations (ICLR) , 2017
D. Tanaka, D. Ikami, T. Yamasaki, and K. Aizawa, “Joint Optimization Framework for Learning with Noisy Labels,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018
2018
Later among the works it cites.
H. Zhang, M. Cisse, Y. Dauphin, and D. Lopez-Paz, “mixup: Beyond Empirical Risk Minimization,” in International Conference on Learning Representations (ICLR) , 2018
2018
Later among the works it cites.
Y. Chen, X. Zhu, and S. Gong, “Semi-Supervised Deep Learning with Memory,” in European Conference on Computer Vision (ECCV) , 2018
2018
Later among the works it cites.
D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep Image Prior,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018
2018
Later among the works it cites.
X. Liu, J. Van De Weijer, and A. D. Bagdanov, “Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2019
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2017
Cited alongside, same era.
W. Liu, R. Lin, Z. Liu, L. Liu, Z. Yu, B. Dai, and L. Song, “Learning towards Minimum Hyperspherical Energy,” in Advances in Neural Information Processing Systems (NeurIPS) , 2018
2018
Cited alongside, same era.
C. Kim, F. Li, and J. Rehg, “Multi-object Tracking with Neural Gating Using Bilinear LSTM,” in European Conference on Computer Vision (ECCV) , 2018
2018
Cited alongside, same era.
S. Xie, C. Sun, J. Huang, Z. Tu, and K. Murphy, “Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification,” in European Conference on Computer Vision (ECCV) , September 2018
2018
Cited alongside, same era.
A. Oliver, A. Odena, C. Raffel, E. Cubuk, and I. Goodfellow, “Realistic Evaluation of Deep Semi-Supervised Learning Algorithms,” in Advances in Neural Information Processing Systems (NeurIPS) , 2018
2018
Cited alongside, same era.
M. Wang and W. Deng, “Deep visual domain adaptation: A survey,” Neurocomputing , vol. 312, pp. 135–153, 2018
2018
Cited alongside, same era.
S. Gidaris, P. Singh, and N. Komodakis, “Unsupervised Representation Learning by Predicting Image Rotations,” in International Conference on Learning Representations (ICLR) , 2018
2018
Cited alongside, same era.
M. González, C. Bergmeir, I. Triguero, Y. Rodríguez, and J. Benítez, “Self-labeling techniques for semi-supervised time series classification: an empirical study,” Knowledge and Information Systems , vol. 55, no. 2, pp. 493–528, 2018
2018
Cited alongside, same era.
2019
Closest in time.
E. Arazo, D. Ortego, P. Albert, N. O’Connor, and K. McGuinness, “Unsupervised Label Noise Modeling and Loss Correction,” in International Conference on Machine Learning (ICML) , 2019
2019
Closest in time.
Y. Li, L. Liu, and R. Tan, “Decoupled Certainty-Driven Consistency Loss for Semi-supervised Learning,” arXiv: 1901.05657 , 2019
2019
Closest in time.
A. Iscen, G. Tolias, Y. Avrithis, and O. Chum, “Label Propagation for Deep Semi-supervised Learning,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Closest in time.
D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, “MixMatch: A Holistic Approach to Semi-Supervised Learning,” in Advances in Neural Information Processing Systems (NeurIPS) , 2019
2019
Closest in time.
S. Thulasidasan, G. Chennupati, J. Bilmes, T. Bhattacharya, and S. Michalak, “On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks,” arXiv: 1905.11001 , 2019
2019
Closest in time.
V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, “Interpolation Consistency Training for Semi-Supervised Learning,” in International Joint Conference on Artificial Intelligence (IJCAI) , 2019
2019
Closest in time.
B. Athiwaratkun, M. Finzi, P. Izmailov, and A. Wilson, “There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average,” in International Conference on Learning Representations (ICLR) , 2019
2019
Closest in time.
Y. Asano, C. Rupprecht, and A. Vedaldi, “Surprising Effectiveness of Few-Image Unsupervised Feature Learning,” in IEEE International Conference on Computer Vision (ICCV) , 2019
2019
Closest in time.
A. Kolesnikov, X. Zhai, and L. Beyer, “Revisiting Self-Supervised Visual Representation Learning,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Closest in time.
D. Ho, E. Liang, X. Chen, I. Stoica, and P. Abbeel, “Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules,” in International Conference on Machine Learning (ICML) , 2019
2019
Closest in time.
S.-A. Rebuffi, S. Ehrhardt, K. Han, A. Vedaldi, and A. Zisserman, “Semi-Supervised Learning with Scarce Annotations,” in IEEE International Conference on Computer Vision (ICCV) , 2019
2019
Closest in time.