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[9] Zhang ZH, Jiang GX, Wang WJ. Label noise filtering method based on dynamic probability sampling. Journal of Computer Applications,
2021, 41(12): 3485–3491 (in Chinese with English abstract). [doi: 10.11772/j.issn.1001-9081.2021061026]
[10] Yu WH, Qin Z. Sampler design for implicit feedback data by noisy-label robust learning. In: Proc. of the 43rd Int’l ACM SIGIR Conf. on
Research and Development in Information Retrieval. ACM, 2020. 861–870. [doi: 10.1145/3397271.3401155]
[11] Yao YZ, Sun ZR, Zhang CY, Shen FM, Wu Q, Zhang J, Tang ZM. Jo-SRC: A contrastive approach for combating noisy labels. In: Proc.
of the 2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021. 5188–5197. [doi: 10.1109/
CVPR46437.2021.00515]
[12] Jiang GX, Wang WJ, Qian YH, Liang JY. A unified sample selection framework for output noise filtering: An error-bound perspective.
Journal of Machine Learning Research, 2021, 22(1): 800–865.
[13] Hou SY, Jiang GX, Wang WJ. A label noise filtering method based on relative outlier factor. Acta Automatica Sinica, 2024, 50(1):
154–168 (in Chinese with English abstract). [doi: 10.16383/j.aas.c230117]
[14] Zhang YK, Zheng SZ, Wu PX, Goswami M, Chen C. Learning with feature-dependent label noise: A progressive approach.
arXiv:2103.07756, 2021.
[15] Zhang J, Jiang XQ, Tian NS, Wu M. Label noise correction for crowdsourcing using dynamic resampling. Engineering Applications of
Artificial Intelligence, 2024, 133: 108439. [doi: 10.1016/j.engappai.2024.108439]
[16] Jiang GX, Zhang J, Bai XF, Wang WJ, Meng DY. Which is more effective in label noise cleaning, correction or filtering? In: Proc. of the
38th AAAI Conf. on Artificial Intelligence. Vancouver: AAAI, 2024. 12866–12873. [doi: 10.1609/aaai.v38i11.29183]
[17] Martín J, Sáez JA, Corchado E. On the regressand noise problem: Model robustness and synergy with regression-adapted noise filters.
IEEE Access, 2021, 9: 145800–145816. [doi: 10.1109/ACCESS.2021.3123151]
[18] Martín J, Sáez JA, Corchado E. Tackling the problem of noisy IoT sensor data in smart agriculture: Regression noise filters for enhanced
evapotranspiration prediction. Expert Systems with Applications, 2024, 237: 121608. [doi: 10.1016/j.eswa.2023.121608]
[19] Kordos M, Białka S, Blachnik M. Instance selection in logical rule extraction for regression problems. In: Proc. of the 12th Int’l Conf. on
Artificial Intelligence and Soft Computing. Zakopane: Springer, 2013. 167–175. [doi: 10.1007/978-3-642-38610-7_16]
[20] Li C, Mao ZZ. A label noise filtering method for regression based on adaptive threshold and noise score. Expert Systems with
Applications, 2023, 228: 120422. [doi: 10.1016/j.eswa.2023.120422]
[21] Li C, Mao ZZ, Jia MX. A real-valued label noise cleaning method based on ensemble iterative filtering with noise score. Int’l Journal of
Machine Learning and Cybernetics, 2024, 15(9): 4093–4118. [doi: 10.1007/s13042-024-02137-z]
[22] Jiang GX, Li ZY, Wang WJ. Maximum a posteriori estimation and filtering algorithm for numerical label noise. Applied Intelligence,
2024, 54(19): 8841–8855. [doi: 10.1007/s10489-024-05648-y]
[23] Jiang GX, Wang WJ. An interpretable sample selection framework against numerical label noise. Machine Learning, 2025, 114(2): 43.
[doi: 10.1007/s10994-024-06637-5]
[24] Bowman AW, Azzalini A. Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-plus Illustrations. New York:
Oxford University Press, 1997.
[25] Asuncion A, Newman D. UCI machine learning repository. 2022. https://archive.ics.uci.edu/datasets
[26] Alcalá-Fdez J, Fernández A, Luengo J, Derrac J, García S, Sánchez L, Herrera F. KEEL data-mining software tool: Data set repository,
integration of algorithms and experimental analysis framework. Journal of Multiple-Valued Logic Soft Computing, 2011, 17: 255–287.
[27] Kuprashevich M, Tolstykh I. MiVOLO: Multi-input Transformer for age and gender estimation. In: Proc. of the 11th Int’l Conf. on
Analysis of Images, Social Networks and Texts. Yerevan: Springer, 2024. 212–226. [doi: 10.1007/978-3-031-54534-4_15]
[28] Abdelrahman AA, Hempel T, Khalifa A, Al-Hamadi A, Dinges L. L2CS-Net: Fine-grained gaze estimation in unconstrained
environments. In: Proc. of the 8th Int’l Conf. on Frontiers of Signal Processing. Corfu: IEEE, 2023. 98–102. [doi: 10.1109/ICFSP59764.
2023.10372944]
[29] Cheng YH, Huang SY, Wang F, Qian C, Lu F. A coarse-to-fine adaptive network for appearance-based gaze estimation. In: Proc. of the
34th AAAI Conf. on Artificial Intelligence. New York: AAAI, 2020. 10623–10630. [doi: 10.1609/aaai.v34i07.6636]
[30] Biswas P. Appearance-based gaze estimation using attention and difference mechanism. In: Proc. of the 2021 IEEE/CVF Conf. on
Computer Vision and Pattern Recognition Workshops. Nashville: IEEE, 2021. 3137–3146. [doi: 10.1109/CVPRW53098.2021.00351]

附中文参考文献
[5] 陈晋音, 周嘉俊, 沈诗婧, 郑海斌, 宣琦. 深度学习人脸识别系统的对抗攻击算法研究. 小型微型计算机系统, 2019, 40(8):
1723–1728. [doi: 10.3969/j.issn.1000-1220.2019.08.028]