COUR T, SAPP B, JORDAN C, et al. Learning from Ambiguously Labeled Images[C] //2009 IEEE Conference on Computer Vision and Pattern Recognition, USA, IEEE, 2009: 919-926.
CHEN C H, PATEL V M, CHELLAPPA R, et al. Learning from Ambiguously Labeled Face Images[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence, 2018, 40(7): 1653-1667. doi: 10.1109/TPAMI.2017.2723401
ZENG Z N, XIAO S J, JIA K, et al. Learning by Associating Ambiguously Labeled Images[J]. Computer Vision and Pattern Recognition, 2013: 708-715.
LUO J, FRANCESCO O. Learning from Candidate Labeling Sets[C] //Neural Information Processing Systems, 2010: 1504-1512.
REN X, HE W Q, QU M, et al. AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label Embedding[J]. Empirical Methods in Natural Language Processing, 2016, 16, 1369-1378.
XIANG R, HE W, MENG Q, et al. Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding[J]. Computing Research Repository, 2016: 1825-1834.
SUN K W, MIN Z J, WANG J. PP-PLL: Probability Propagation for Partial Label Learning[C] //European Conference on Principles of Data Mining and Knowledge Discovery, 2019: 123-137.
YU F, ZHANG M L. Maximum Margin Partial Label Learning[J]. Asian Conference on Machine Learning, 2017, 106(4): 573-593. doi: 10.1007/s10994-016-5606-4
NGUYEN N, CARUANA R. Classification with Partial Labels[C] //In Proceedings of the 14th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, 2008: 551-559.
ZHANG M L, YU F, TANG C Z. Disambiguation-Free Partial Label Learning[C] //IEEE Transactionson Knowledgeand Data Engineering. IEEE, 2017: 2155-2167.
WANG H B, XIAO R X, LI Y X, et al. PiCO: Contrastive Label Disambiguation for Partial Label Learning[C] //International Conference on Learning Representations, 2022.
WEN H W, CUI J Y, HANG H Y, et al. Leveraged Weighted Loss for Partial Label Learning[C] International Conference on Machine Learning, 2021, 139: 11091-11100.
LV J Q, XU M, FENG L, et al. Progressive Identification of True Labels for Partial-Label Learning[C] //Proceedings of the 37th International Conference on Machine Learning. ACM, 2020: 6500-6510.
FENG L, LYU J Q, HAN B, et al. Provably Consistent Partial-Label Learning[EB/OL]. (2020-10-23)[2023-04-20]. https://arxiv.org/pdf/2007.08929.pdf.
WU D D, WANG D B, ZHANG M L. Revisiting Consistency Regularization for Deep Partial Label Learning[C] International Conference on Machine Learning, 2022: 24212-24225.
WANG Q W, LI Y F, ZHOU Z H. Partial Label Learning with Unlabeled Data[C] International Joint Conference on Artificial Intelligence, 2019: 3755-3761.
LI Y, LIU C, ZHAO S Y, et al. Active Partial Label Learning Based on Adaptive Sample Selection[J]. International Journal of Machine Learning and Cybernetics, 2022, 13(6): 1603-1617. doi: 10.1007/s13042-021-01470-x
KIHYUK S, DAVID B, NICHOLAS C, et al. FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence[C] Neural Information Processing Systems, 2020: 596-608.
ZHANG B W, WANGY D, HOU W X, et al. FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling[C] Neural Information Processing Systems, 2021: 18408-18419.
LEE D H. PSEUDOL. TheSimple and Efficient Semi-Supervised Learning Method for Deep Neural Networks[C] //Workshop on challenges in representation learning, ICML, 2013, 3(2): 896.
MIYATO T, MAEDASI, KOYAMAM, et al. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence, 2019, 41(8): 1979-1993. doi: 10.1109/TPAMI.2018.2858821
EKIN D C, BARRET Z, JONATHON S, et al. Randaugment: Practical automated data augmentation with a reduced search space[C] Computer Vision and Pattern Recognition, 2020: 3008-3017.
EKIN D C, BARRET Z, DANDELION M, et al. Auto Augment: Learning Augmentation Strategies From Data[C] Computer Vision and Pattern Recognition, 2019: 113-123.
LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-Based Learning Applied to Document Recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. doi: 10.1109/5.726791
XIAO H, RASUL K, VOLLGRAF R. Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms[J]. ArXive-Prints, 2017: 07747.
NETZER Y, WANG T, COATES A, et al. Reading Digits in Natural Images with Unsupervised Feature Learning[J]. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011: 067128.
KRIZHEVSKY A, HINTON G. Learning Multiple Layers of Features from Tiny Images[J]. Handbook of Systemic Autoimmune Diseases, 2009, 1(4): 18268744.