Graph TopoFilter: A Method for Noisy Labels Detection for Graph-Structured Classes
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-36024-4_51
Reference13 articles.
1. Cordeiro, F.R., Carneiro, G.: A survey on deep learning with noisy labels: how to train your model when you cannot trust on the annotations? In: 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 9–16. IEEE (2020)
2. Fan, J., Rao, S., Muniraju, G., Tepedelenlioglu, C., Spanias, A.: Fault classification in photovoltaic arrays using graph signal processing. In: 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), vol. 1, pp. 315–319. IEEE (2020)
3. de Freitas, J.T., Coelho, F.G.F.: Fault localization method for power distribution systems based on gated graph neural networks. Electr. Eng., 1–8 (2021)
4. Frénay, B., Verleysen, M.: Classification in the presence of label noise: a survey. IEEE Trans. Neural Netw. Learn. Syst. 25(5), 845–869 (2013)
5. Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. arXiv preprint arXiv:1804.06872 (2018)
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