1. Berthelot, D., Carlini, N., Goodfellow, I.G., Papernot, N., Oliver, A., Raffel, C.: Mixmatch: a holistic approach to semi-supervised learning. In: NeurIPS (2019)
2. Bowyer, K.W., Chawla, N.V., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
3. Bulò, S.R., Neuhold, G., Kontschieder, P.: Loss max-pooling for semantic image segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7082–7091 (2017)
4. Byrd, J., Lipton, Z.C.: What is the effect of importance weighting in deep learning? In: ICML (2018)
5. Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: Advances in Neural Information Processing Systems (2019)