Open Access
| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 8 | |
| Section | Intelligent Computing in Healthcare and Bioinformatics | |
| DOI | https://doi.org/10.1051/itmconf/20268401004 | |
| Published online | 06 April 2026 | |
- A. A. Gharawi, J. Alsubhi and L. Ramaswamy, Impact of Labeling Noise on Machine Learning: A Cost-aware Empirical Study, 21st IEEE International Conference on Machine Learning and Applications (ICMLA), Nassau, Bahamas. (2022) [Google Scholar]
- C. Gong et al., Class-Wise Denoising for Robust Learning Under Label Noise, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 2835–2848, 1 March, doi: 10.1109/TPAMI.2022.3178690. (2023) [Google Scholar]
- S. Reed, H. Lee, C. Zhang, & D. D. Lee. Training Deep Neural Networks on Noisy Labels with Bootstrapping. In Advances in neural information processing systems (pp. 644-652). (2015) [Google Scholar]
- S. Sukhbaatar, A. Szlam, J. Weston, & R. Fergus. End-To-End Memory Networks. arXiv preprint arXiv:1503.08895. (2015) [Google Scholar]
- A. Ghosh, H. Kumar, & P. Sastry. Robust loss functions under label noise for deep neural networks. In Proceeding of the Conference on Artificial Intelligence Vol. 31, No. 1). (2017) [Google Scholar]
- Z. Zhang, & M. R. Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in Neural Information Processing Systems, 31. (2018) [Google Scholar]
- Y. Xu, P. Cao, Y. Kong, & Y. Wang. L_DMI: A novel information-theoretic loss function for training deep nets robust to label noise. Advances in Neural Information Processing Systems, 32. (2019) [Google Scholar]
- D. Rolnick, A. Veit, S. Belongie, and N. Shavit, Deep learning is robust to massive label noise. arXiv:1705.10694. [Online]. Available: https://arxiv.org/abs/1705.10694. (2017) [Google Scholar]
- B. Gao, H. Gouk and T. M. Hospedales, Searching for Robustness: Loss Learning for Noisy Classification Tasks,” 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, pp. 6650–6659, doi: 10.1109/ICCV48922.2021.00660. (2021) [Google Scholar]
- M. Ren, Y. Zou, X. Wang, & H. Li. Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting. Proceedings of the 35th International Conference on Machine Learning (ICML 2018). (2018) [Google Scholar]
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