Smoothing and Transition Matrices Estimation to Learn with Noisy Labels

Author:

Ricci SimoneORCID,Uricchio TiberioORCID,Del Bimbo AlbertoORCID

Publisher

Springer Nature Switzerland

Reference38 articles.

1. Azadi, S., Feng, J., Jegelka, S., Darrell, T.: Auxiliary image regularization for deep CNNs with noisy labels. arXiv preprint arXiv:1511.07069 (2015)

2. Bahri, D., Jiang, H., Gupta, M.: Deep K-NN for noisy labels. In: International Conference on Machine Learning, pp. 540–550. PMLR (2020)

3. Chen, P., Liao, B.B., Chen, G., Zhang, S.: Understanding and utilizing deep neural networks trained with noisy labels. In: International Conference on Machine Learning, pp. 1062–1070. PMLR (2019)

4. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

5. Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126–1135. PMLR (2017)

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