Cross-to-merge training with class balance strategy for learning with noisy labels

Author:

Zhang QianORCID,Zhu YiORCID,Yang Ming,Jin Ge,Zhu YingWen,Chen QiuORCID

Funder

Natural Science Research of Jiangsu Higher Education Institutions of China

Japan Society for the Promotion of Science

Major Basic Research Project of the Natural Science Foundation of the Jiangsu Higher Education Institutions

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference71 articles.

1. Arazo, E., Ortego, D., Albert, P., O’Connor, N., & McGuinness, K. (2019). Unsupervised label noise modeling and loss correction. In International conference on machine learning, 97, 312-321. URL: https://proceedings.mlr.press/v97/arazo19a.html.

2. Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., & Bengio, Y. (2017). A closer look at memorization in deep networks. In International conference on machine learning, 80, 233-242. URL: https://proceedings.mlr.press/v70/arpit17a.html.

3. FRED-Net: Fully residual encoder–decoder network for accurate iris segmentation;Arsalan;Expert Systems with Applications,2019

4. Bai, Y., & Liu, T. (2021). Me-momentum: Extracting hard confident examples from noisily labeled data. In Proceedings of the IEEE/CVF international conference on computer vision, 9312-9321. doi:10.1109/ICCV48922.2021.00918.

5. Mixmatch: A holistic approach to semi-supervised learning;Berthelot,2019

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