Sample Selection Based on Uncertainty for Combating Label Noise

Author:

Hao Shuohui,Liu Zhe,Song Yuqing,Liu Yi,Han Kai,Sheng Victor S.,Zhu Yan

Publisher

Springer Nature Singapore

Reference22 articles.

1. Arazo, E., Ortego, D., Albert, P., O’Connor, N., McGuinness, K.: Unsupervised label noise modeling and loss correction. In: International Conference on Machine Learning, pp. 312–321. PMLR (2019)

2. Ching, T., Himmelstein, D.S., Beaulieu-Jones, B.K., Kalinin, A.A., Do, B.T., Way, G.P., Ferrero, E., Agapow, P.M., Zietz, M., Hoffman, M.M., et al.: Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15(141), 20170387 (2018)

3. Falk, T., et al.: U-net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16(1), 67–70 (2019)

4. Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. In: Advances in Neural Information Processing Systems 31 (2018)

5. Hollon, T.C., et al.: Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nature Med. 26(1), 52–58 (2020)

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