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Semi-supervised learning (SSL) has long been proved to be an effective technique to construct powerful models with limited labels.
I. Bekkerman and J. Tabrikian, “Target detection and localization using mimo radars and sonars,” IEEE Transactions on Signal Processing , vol. 54, no. 10, pp. 3873–3883, 2006
2006
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” 2011
2011
Earlier work this paper cites.
Z. Zhang, T. W. Chow, and M. Zhao, “Trace ratio optimization-based semi-supervised nonlinear dimensionality reduction for marginal manifold visualization,” IEEE Transactions on Knowledge and Data Engineering , vol. 25, no. 5, pp. 1148–1161, 2012
2012
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge: A retrospective,” International journal of computer vision , vol. 111, no. 1, pp. 98–136, 2015
2015
Earlier work this paper cites.
Q. Ye, J. Yang, T. Yin, and Z. Zhang, “Can the virtual labels obtained by traditional lp approaches be well encoded in wlr?” IEEE transactions on neural networks and learning systems , vol. 27, no. 7, pp. 1591–1598, 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Sajjadi, M. Javanmardi, and T. Tasdizen, “Regularization with stochastic transformations and perturbations for deep semi-supervised learning,” Advances in neural information processing systems , vol. 29, pp. 1163–1171, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Sajjadi, M. Javanmardi, and T. Tasdizen, “Regularization with stochastic transformations and perturbations for deep semi-supervised learning,” Advances in neural information processing systems , vol. 29, pp. 1163–1171, 2016
2016
Earlier work this paper cites.
M. Luo, X. Chang, L. Nie, Y. Yang, A. G. Hauptmann, and Q. Zheng, “An adaptive semisupervised feature analysis for video semantic recognition,” IEEE transactions on cybernetics , vol. 48, no. 2, pp. 648–660, 2017
2017
Earlier work this paper cites.
Z. Zhang, F. Li, L. Jia, J. Qin, L. Zhang, and S. Yan, “Robust adaptive embedded label propagation with weight learning for inductive classification,” IEEE transactions on neural networks and learning systems , vol. 29, no. 8, pp. 3388–3403, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. Van Der Maaten, “Exploring the limits of weakly supervised pretraining,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 181–196
2018
Earlier work this paper cites.
E. Yu, J. Sun, J. Li, X. Chang, X.-H. Han, and A. G. Hauptmann, “Adaptive semi-supervised feature selection for cross-modal retrieval,” IEEE Transactions on Multimedia , vol. 21, no. 5, pp. 1276–1288, 2018
2018
Earlier work this paper cites.
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin, “Unsupervised feature learning via non-parametric instance discrimination,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 3733–3742
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
T. Miyato, S.-i. Maeda, M. Koyama, and S. Ishii, “Virtual adversarial training: a regularization method for supervised and semi-supervised learning,” IEEE transactions on pattern analysis and machine intelligence , vol. 41, no. 8, pp. 1979–1993, 2018
2018
Cited alongside, same era.
T. Lucas, C. Tallec, Y. Ollivier, and J. Verbeek, “Mixed batches and symmetric discriminators for gan training,” in International Conference on Machine Learning . PMLR, 2018, pp. 2844–2853
2018
Cited alongside, same era.
Y. Tokozume, Y. Ushiku, and T. Harada, “Between-class learning for image classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 5486–5494
2018
Cited alongside, same era.
H. Zhang, Z. Zhang, M. Zhao, Q. Ye, M. Zhang, and M. Wang, “Robust triple-matrix-recovery-based auto-weighted label propagation for classification,” IEEE Transactions on Neural Networks and Learning Systems , vol. 31, no. 11, pp. 4538–4552, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning . PMLR, 2020, pp. 1597–1607
2020
Later among the works it cites.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9729–9738
2020
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2018
Cited alongside, same era.
2018
Cited alongside, same era.
S. Zhou, D. Nie, E. Adeli, J. Yin, J. Lian, and D. Shen, “High-resolution encoder–decoder networks for low-contrast medical image segmentation,” IEEE Transactions on Image Processing , vol. 29, pp. 461–475, 2019
2019
Cited alongside, same era.
S. Zhou, X. Liu, M. Li, E. Zhu, L. Liu, C. Zhang, and J. Yin, “Multiple kernel clustering with neighbor-kernel subspace segmentation,” IEEE transactions on neural networks and learning systems , vol. 31, no. 4, pp. 1351–1362, 2019
2019
Cited alongside, same era.
S. Wang, X. Liu, E. Zhu, C. Tang, J. Liu, J. Hu, J. Xia, and J. Yin, “Multi-view clustering via late fusion alignment maximization.” in IJCAI , 2019, pp. 3778–3784
2019
Cited alongside, same era.
K. Chen, L. Yao, D. Zhang, X. Wang, X. Chang, and F. Nie, “A semisupervised recurrent convolutional attention model for human activity recognition,” IEEE transactions on neural networks and learning systems , vol. 31, no. 5, pp. 1747–1756, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Later among the works it cites.
2020
Later among the works it cites.
P. Chen, T. Ma, X. Qin, W. Xu, and S. Zhou, “Data-efficient semi-supervised learning by reliable edge mining,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9192–9201
2020
Later among the works it cites.
H. Guo, “Nonlinear mixup: Out-of-manifold data augmentation for text classification,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 04, 2020, pp. 4044–4051
2020
Later among the works it cites.
X. Hu, Y. Zeng, X. Xu, S. Zhou, and L. Liu, “Robust semi-supervised classification based on data augmented online elms with deep features,” Knowledge-Based Systems , vol. 229, p. 107307, 2021
2021
Later among the works it cites.
S. Wang, X. Liu, L. Liu, S. Zhou, and E. Zhu, “Late fusion multiple kernel clustering with proxy graph refinement,” IEEE Transactions on Neural Networks and Learning Systems , 2021
2021
Later among the works it cites.
S. Wang, X. Liu, X. Zhu, P. Zhang, Y. Zhang, F. Gao, and E. Zhu, “Fast parameter-free multi-view subspace clustering with consensus anchor guidance,” IEEE Transactions on Image Processing , vol. 31, pp. 556–568, 2021
2021
Later among the works it cites.
J. Li, C. Xiong, and S. C. Hoi, “Comatch: Semi-supervised learning with contrastive graph regularization,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 9475–9484
2021
Later among the works it cites.
2021
Later among the works it cites.
X. Chen and K. He, “Exploring simple siamese representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 15 750–15 758
2021
Later among the works it cites.
R. He, Z. Han, X. Lu, and Y. Yin, “Safe-student for safe deep semi-supervised learning with unseen-class unlabeled data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 14 585–14 594
2022
Closest in time.
L. Li, S. Wang, X. Liu, E. Zhu, L. Shen, K. Li, and K. Li, “Local sample-weighted multiple kernel clustering with consensus discriminative graph,” IEEE Transactions on Neural Networks and Learning Systems , 2022
2022
Closest in time.
S. Wang, X. Liu, L. Liu, W. Tu, X. Zhu, J. Liu, S. Zhou, and E. Zhu, “Highly-efficient incomplete large-scale multi-view clustering with consensus bipartite graph,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 9776–9785
2022
Closest in time.
R. He, Z. Han, and Y. Yin, “Towards safe and robust weakly-supervised anomaly detection under subpopulation shift,” Knowledge-Based Systems , p. 109088, 2022
2022
Closest in time.
Y. Liu, W. Tu, S. Zhou, X. Liu, L. Song, X. Yang, and E. Zhu, “Deep graph clustering via dual correlation reduction,” in Proc. of AAAI , 2022
2022
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