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Partial label learning (PLL) is a typical weakly supervised learning, where each sample is associated with a set of candidate labels.
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2008
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T. Cour, B. Sapp, and B. Taskar, “Learning from partial labels,” Journal of Machine Learning Research , vol. 12, pp. 1501–1536, 2011
2011
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L.-P. Liu and T. G. Dietterich, “A conditional multinomial mixture model for superset label learning,” in Proceedings of the 25th International Conference on Neural Information Processing Systems , 2012, pp. 548–556
2012
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F. Briggs, X. Z. Fern, and R. Raich, “Rank-loss support instance machines for miml instance annotation,” in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2012, pp. 534–542
2012
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J. Cid-Sueiro, “Proper losses for learning from partial labels,” in Proceedings of the 25th International Conference on Neural Information Processing Systems , 2012, pp. 1565–1573
2012
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Y.-C. Chen, V. M. Patel, R. Chellappa, and P. J. Phillips, “Ambiguously labeled learning using dictionaries,” IEEE Transactions on Information Forensics and Security , vol. 9, no. 12, pp. 2076–2088, 2014
2014
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L. Liu and T. Dietterich, “Learnability of the superset label learning problem,” in Proceedings of the International Conference on Machine Learning , 2014, pp. 1629–1637
2014
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E. Hüllermeier and W. Cheng, “Superset learning based on generalized loss minimization,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases , 2015, pp. 260–275
2015
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M.-L. Zhang and F. Yu, “Solving the partial label learning problem: an instance-based approach,” in Proceedings of the 24th International Conference on Artificial Intelligence , 2015, pp. 4048–4054
2015
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F. Yu and M.-L. Zhang, “Maximum margin partial label learning,” in Proceedings of the Asian Conference on Machine Learning , 2016, pp. 96–111
2016
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M.-L. Zhang, B.-B. Zhou, and X.-Y. Liu, “Partial label learning via feature-aware disambiguation,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2016, pp. 1335–1344
2016
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C.-H. Chen, V. M. Patel, and R. Chellappa, “Learning from ambiguously labeled face images,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 40, no. 7, pp. 1653–1667, 2017
2017
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C.-Z. Tang and M.-L. Zhang, “Confidence-rated discriminative partial label learning,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence , 2017, pp. 2611–2617
2017
Cited alongside, same era.
C. Gong, T. Liu, Y. Tang, J. Yang, J. Yang, and D. Tao, “A regularization approach for instance-based superset label learning,” IEEE Transactions on Cybernetics , vol. 48, no. 3, pp. 967–978, 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,” Advances in neural information processing systems , pp. 1–10, 2017
2017
Cited alongside, same era.
D. Hendrycks and K. Gimpel, “A baseline for detecting misclassified and out-of-distribution examples in neural networks,” in Proceedings of the International Conference on Learning Representations , 2017, pp. 1–12
2017
Cited alongside, same era.
Y. Yao, J. Deng, X. Chen, C. Gong, J. Wu, and J. Yang, “Deep discriminative cnn with temporal ensembling for ambiguously-labeled image classification,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2020, pp. 12 669–12 676
2020
Later among the works it cites.
J. Lv, M. Xu, L. Feng, G. Niu, X. Geng, and M. Sugiyama, “Progressive identification of true labels for partial-label learning,” in Proceedings of the International Conference on Machine Learning , 2020, pp. 6500–6510
2020
Later among the works it cites.
L. Feng, J. Lv, B. Han, M. Xu, G. Niu, X. Geng, B. An, and M. Sugiyama, “Provably consistent partial-label learning,” in Proceedings of the Advances in Neural Information Processing Systems , 2020, pp. 10 948–10 960
2020
Later among the works it cites.
2020
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S. Li, W. Deng, and J. Du, “Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2852–2861
2017
Cited alongside, same era.
Z.-H. Zhou, “A brief introduction to weakly supervised learning,” National Science Review , vol. 5, no. 1, pp. 44–53, 2018
2018
Cited alongside, same era.
L. Feng and B. An, “Leveraging latent label distributions for partial label learning.” in Proceedings of the International Joint Conference on Artificial Intelligence , 2018, pp. 2107–2113
2018
Cited alongside, same era.
Z. Zhang and M. R. Sabuncu, “Generalized cross entropy loss for training deep neural networks with noisy labels,” in Proceedings of the 32nd International Conference on Neural Information Processing Systems , 2018, pp. 8792–8802
2018
Cited alongside, same era.
L. Jiang, Z. Zhou, T. Leung, L.-J. Li, and L. Fei-Fei, “Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels,” in Proceedings of the International Conference on Machine Learning , 2018, pp. 2304–2313
2018
Cited alongside, same era.
B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. W. Tsang, and M. Sugiyama, “Co-teaching: robust training of deep neural networks with extremely noisy labels,” in Proceedings of the 32nd International Conference on Neural Information Processing Systems , 2018, pp. 8536–8546
2018
Cited alongside, same era.
S. Liang, Y. Li, and R. Srikant, “Enhancing the reliability of out-of-distribution image detection in neural networks,” in Proceedings of the International Conference on Learning Representations , 2018, pp. 1–27
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Later among the works it cites.
S. Liu, J. Niles-Weed, N. Razavian, and C. Fernandez-Granda, “Early-learning regularization prevents memorization of noisy labels,” Advances in neural information processing systems , pp. 20 331–20 342, 2020
2020
Later among the works it cites.
J. Li, R. Socher, and S. C. Hoi, “Dividemix: Learning with noisy labels as semi-supervised learning,” in Proceedings of the International Conference on Learning Representations , 2020, pp. 1–14
2020
Later among the works it cites.
L. Feng, T. Kaneko, B. Han, G. Niu, B. An, and M. Sugiyama, “Learning with multiple complementary labels,” in Proceedings of the International Conference on Machine Learning . PMLR, 2020, pp. 3072–3081
2020
Later among the works it cites.
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, “Supervised contrastive learning,” in Proceedings of the Advances in Neural Information Processing Systems , 2020, pp. 18 661–18 673
2020
Later among the works it cites.
F. Zhang, L. Feng, B. Han, T. Liu, G. Niu, T. Qin, and M. Sugiyama, “Exploiting class activation value for partial-label learning,” in Proceedings of the International Conference on Learning Representations , 2021, pp. 1–17
2021
Later among the works it cites.
N. Xu, C. Qiao, X. Geng, and M.-L. Zhang, “Instance-dependent partial label learning,” in Proceedings of the Advances in Neural Information Processing Systems , 2021, pp. 27 119–27 130
2021
Later among the works it cites.
H. Wen, J. Cui, H. Hang, J. Liu, Y. Wang, and Z. Lin, “Leveraged weighted loss for partial label learning,” in Proceedings of the International Conference on Machine Learning . PMLR, 2021, pp. 11 091–11 100
2021
Later among the works it cites.
Y. Zhang, S. Zheng, P. Wu, M. Goswami, and C. Chen, “Learning with feature-dependent label noise: A progressive approach,” in Proceedings of the International Conference on Learning Representations , 2021, pp. 1–13
2021
Later among the works it cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of Machine Learning Research , vol. 9, pp. 2579–2605, 2008
2021
Later among the works it cites.
D.-D. Wu, D.-B. Wang, and M.-L. Zhang, “Revisiting consistency regularization for deep partial label learning,” in Proceedings of the International Conference on Machine Learning , 2022, pp. 24 212–24 225
2022
Closest in time.
S.-Y. Xia, J. Lv, N. Xu, and X. Geng, “Ambiguity-induced contrastive learning for instance-dependent partial label learning,” in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI , 2022, pp. 3615–3621
2022
Closest in time.
H. Wang, R. Xiao, Y. Li, L. Feng, G. Niu, G. Chen, and J. Zhao, “Pico: Contrastive label disambiguation for partial label learning,” in Proceedings of the International Conference on Learning Representations , 2022, pp. 1–18
2022
Closest in time.
H. Song, M. Kim, D. Park, Y. Shin, and J.-G. Lee, “Learning from noisy labels with deep neural networks: A survey,” IEEE Transactions on Neural Networks and Learning Systems , 2022
2022
Closest in time.
2022
Closest in time.
D.-B. Wang, M.-L. Zhang, and L. Li, “Adaptive graph guided disambiguation for partial label learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 12, pp. 8796–8811, 2022
2022
Closest in time.
J. Lv, B. Liu, L. Feng, N. Xu, M. Xu, B. An, G. Niu, X. Geng, and M. Sugiyama, “On the robustness of average losses for partial-label learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Closest in time.
H. Wang, R. Xiao, Y. Li, L. Feng, G. Niu, G. Chen, and J. Zhao, “Pico+: Contrastive label disambiguation for robust partial label learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 5, pp. 3183–3198, 2023
2023
Closest in time.