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Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is correct.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Learning with multiple labels
Jin, R. and Ghahramani, Z · 2003
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Convexity, classification, and risk bounds
Bartlett, P. L., Jordan, M. I., and McAuliffe, J. D · 2006
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Learning from ambiguously labeled examples
Hüllermeier, E. and Beringer, J · 2006
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E · 2007
Earlier work this paper cites.
The mir flickr retrieval evaluation
Huiskes, M. J. and Lew, M. S · 2008
Earlier work this paper cites.
Classification with partial labels
Nguyen, N. and Caruana, R · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Multiple instance metric learning from automatically labeled bags of faces
Guillaumin, M., Verbeek, J., and Schmid, C · 2010
Earlier work this paper cites.
Learning from candidate labeling sets
Luo, J. and Orabona, F · 2010
Earlier work this paper cites.
Composite binary losses
Reidand, M. D. and Williamson, R. C · 2010
Earlier work this paper cites.
Learning from partial labels
Cour, T., Sapp, B., and Taskar, B · 2011
Earlier work this paper cites.
Rank-loss support instance machines for miml instance annotation
Briggs, F., Fern, X. Z., and Raich, R · 2012
Earlier work this paper cites.
A conditional multinomial mixture model for superset label learning
Liu, L. and Dietterich, T. G · 2012
Earlier work this paper cites.
Learning a part-of-speech tagger from two hours of annotation
Garrette, D. and Baldridge, J · 2013
Cited alongside, same era.
Learning by associating ambiguously labeled images
Zeng, Z., Xiao, S., Jia, K., Chan, T., Gao, S., Xu, D., and Ma, Y · 2013
Cited alongside, same era.
Ambiguously labeled learning using dictionaries
Chen, Y., Patel, V. M., Pillai, J. K., Chellappa, R., and Phillips, P. J · 2014
Cited alongside, same era.
Solving the partial label learning problem: An instance-based approach
Zhang, M. and Yu, F · 2015
Cited alongside, same era.
Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
Partial label learning via feature-aware disambiguation
Zhang, M., Zhou, B., and Liu, X · 2016
Learning with biased complementary labels
Yu, X., Liu, T., Gong, M., and Tao, D · 2018
Later among the works it cites.
Partial label learning with self-guided retraining
Feng, L. and An, B · 2019
Later among the works it cites.
Complementary-label learning for arbitrary losses and models
Ishida, T., Niu, G., Menon, A. K., and Sugiyama, M · 2019
Later among the works it cites.
Partial label learning via label enhancement
Xu, N., Lv, J., and Geng, X · 2019
Later among the works it cites.
Progressive identification of true labels for partial-label learning
Lv, J., Xu, M., Feng, L., Niu, G., Geng, X., and Sugiyama, M · 2020
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Part-dependent label noise: Towards instance-dependent label noise
Xia, X., Liu, T., Han, B., Wang, N., Gong, M., Liu, H., Niu, G., Tao, D., and Sugiyama, M · 2020
Later among the works it cites.
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Cited alongside, same era.
Variational inference: A review for statisticians
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D · 2017
Cited alongside, same era.
Learning from complementary labels
Ishida, T., Niu, G., Hu, W., and Sugiyama, M · 2017
Cited alongside, same era.
Making deep neural networks robust to label noise: A loss correction approach
Patrini, G., Rozza, A., Menon, A. K., Nock, R., and Qu, L · 2017
Cited alongside, same era.
Confidence-rated discriminative partial label learning
Tang, C. and Zhang, M · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Cited alongside, same era.
Deep learning for fixed model reuse
Yang, Y., Zhan, D., Fan, Y., Jiang, Y., and Zhou, Z · 2017
Cited alongside, same era.
Leveraged weighted loss for partial label learning
Wen, H., Cui, J., Hang, H., Liu, J., Wang, Y., and Lin, Z · 2021
Later among the works it cites.
Learning with proper partial labels
Wu, Z. and Sugiyama, M · 2021
Later among the works it cites.
Partial label learning by semantic difference maximization
Lyu, G., Wu, Y., and Feng, S · 2022
Closest in time.
Pico: Contrastive label disambiguation for partial label learning
Wang, H., Xiao, R., Li, Y., Feng, L., Niu, G., Chen, G., and Zhao, J · 2022
Closest in time.
Revisiting consistency regularization for deep partial label learning
Wu, D.-D., Wang, D.-B., and Zhang, M.-L · 2022
Closest in time.
One positive label is sufficient: Single-positive multi-label learning with label enhancement
Xu, N., Qiao, C., Lv, J., Geng, X., and Zhang, M.-L · 2022
Closest in time.
Variational label enhancement
Xu, N., Shu, J., Zheng, R., Geng, X., Meng, D., and Zhang, M · 2023
Closest in time.