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Deep learning methods have achieved promising performance in many areas, but they are still struggling with noisy-labeled images during the training process.
1901
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1907
Earlier work this paper cites.
Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., and Qu, L. (2017). Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1944-1952)
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Ronneberger, O., Fischer, P., and Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham
2015
Cited alongside, same era.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778)
2016
Cited alongside, same era.
Goldberger, J., and Ben-Reuven, E. (2016). Training deep neural-networks using a noise adaptation layer
2016
Cited alongside, same era.
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals, Understanding deep learning requires rethinking generalization, in ICLR, 2017
2017
Cited alongside, same era.
2017
Later among the works it cites.
Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., and Belongie, S. (2017). Learning from noisy large-scale datasets with minimal supervision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 839-847)
2017
Later among the works it cites.
Tanaka, D., Ikami, D., Yamasaki, T., and Aizawa, K. (2018). Joint optimization framework for learning with noisy labels. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5552-5560)
2018
Later among the works it cites.
Dgani, Y., Greenspan, H., and Goldberger, J. (2018, April). Training a neural network based on unreliable human annotation of medical images. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 39-42). IEEE
2018
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