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