1. Learning from crowds with multiple noisy label distribution propagation;Jiang;IEEE Trans. Neural Netw. Learn. Syst.,2021
2. Neighborhood weighted voting-based noise correction for crowdsourcing;Li;ACM Trans. Knowl. Discov. Data,2023
3. Label distribution similarity-based noise correction for crowdsourcing;Ren;Front. Comput. Sci.,2024
4. T. Xiao, T. Xia, Y. Yang, C. Huang, X. Wang, Learning from massive noisy labeled data for image classification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2691–2699.
5. Self-paced resistance learning against overfitting on noisy labels;Shi;Pattern Recognit.,2023