[1] [1] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Image classification with deep convolutional neural wks[C]Proceedings of the 25th International Conference on Neural Infmation Processing Systems. Lake Tahoe: Curran Associates Inc., 2012.

[2] [2] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning f image recognition[C]Proceedings of 2016 IEEE Conference on Computer Vision Pattern Recognition. Las Vegas: IEEE, 2016: 770 − 778.

[4] [4] LI W, WANG L M, LI W, et al. WebVision database: visual learning understing from web data[J]. arXiv preprint, arXiv: 1708.02862, 2017.

[5] [5] SUKHBAATAR S, BRUNA J, PALURI M, et al. Training convolutional wks with noisy labels[J]. arXiv preprint, arXiv: 1406.2080, 2014.

[6] [6] GHOSH A, KUMAR H, SASTRY P S. Robust loss functions under label noise f deep neural wks[C]Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco: AAAI, 2017: 1919−1925.

[7] [7] HAN B, YAO Q M, YU X R, et al. Coteaching: robust training of deep neural wks with extremely noisy labels[C]Proceedings of the 32nd International Conference on Neural Infmation Processing Systems. Montréal: Curran Associates Inc., 2018.

[8] [8] KARIM N, RIZVE M N, RAHNAVARD N, et al. UNICON: combating label noise through unifm ion contrastive learning[C]Proceedings of 2022 IEEECVF Conference on Computer Vision Pattern Recognition. New leans: IEEE, 2022: 9676 − 9686.

[9] [9] SHARMA K, DONMEZ P, LUO E M, et al. NoiseRank: unsupervised label noise reduction with dependence models[C]Proceedings of the 16th European Conference on Computer Vision. Glasgow: Springer, 2020: 737 − 753.

[10] [10] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss f dense object detection[C]Proceedings of 2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2980 − 2988.

[11] [11] ZHANG Z L, SABUNCU M R. Generalized cross entropy loss f training deep neural wks with noisy labels[C]Proceedings of the 32nd International Conference on Neural Infmation Processing Systems. Montréal: Curran Associates Inc., 2018.

[12] [12] LI J N, SOCHER R, HOI S C H. DivideMix: learning with noisy labels as semisupervised learning[C]Proceedings of the 8th International Conference on Learning Representations. Addis Ababa: OpenReview, 2020.

[13] [13] BERTHELOT D, CARLINI N, GOODFELLOW I, et al. MixMatch: a holistic approach to semisupervised learning[C]Proceedings of the 33rd International Conference on Neural Infmation Processing Systems. Vancouver: Curran Associates Inc., 2019.

[14] [14] CUBUK E D, ZOPH B, SHLENS J, et al. Raugment: practical automated data augmentation with a reduced search space[C]Proceedings of 2020 IEEECVF Conference on Computer Vision Pattern Recognition.Seattle: IEEE, 2020: 702 − 703.

[15] [15] ZHANG H Y, CISSÉ M, DAUPHIN Y N, et al. Mixup: beyond empirical risk minimization[C]Proceedings of the 6th International Conference on Learning Representations. Vancouver: OpenReview, 2018.

[16] [16] YANG F, WU K, ZHANG S Y, et al. Classaware contrastive semisupervised learning[C]Proceedings of 2022 IEEECVF Conference on Computer Vision Pattern Recognition. New leans: IEEE, 2022: 14421 − 14430.

[17] [17] SOHN K, BERTHELOT D, LI C L, et al. FixMatch: simplifying semisupervised learning with consistency confidence[C]Proceedings of the 34th International Conference on Neural Infmation Processing Systems. Vancouver: Curran Associates Inc., 2020: 596 − 608.

[18] [18] YU X R, HAN B, YAO J C, et al. How does disagreement help generalization against label cruption[C]Proceedings of the 36th International Conference on Machine Learning. Long Beach: PMLR, 2019: 7164 − 7173.

[19] [19] YI K, WU J X. Probabilistic endtoend noise crection f learning with noisy labels[C]Proceedings of 2019 IEEECVF Conference on Computer Vision Pattern Recognition. Long Beach: IEEE, 2019: 7017 − 7025.

[20] [20] LI J N, WONG Y, ZHAO Q, et al. Learning to learn from noisy labeled data[C]Proceedings of 2019 IEEECVF Conference on Computer Vision Pattern Recognition. Long Beach: IEEE, 2019: 5051 − 5059.

[21] [21] ARAZO E, TEGO D, ALBERT P, et al. Unsupervised label noise modeling loss crection[C]Proceedings of the 36th International Conference on Machine Learning. Long Beach: PMLR, 2019: 312 − 321.

[22] MIAO Q, WU X H, XU C et al. Learning with noisy labels using collaborative sample selection and contrastive semi-supervised learning[J]. Knowledge-Based Systems, 296, 111860(2024).