1. ACE: Ally complementary experts for solving long-tailed recognition in one-shot;Cai,2021
2. Cao, K., Wei, C., Gaidon, A., Aréchiga, N., Ma, T., 2019. Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E.B., Garnett, R. (Eds.), Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019. NeurIPS 2019, December 8–14, 2019, Vancouver, BC, Canada, pp. 1565–1576.
3. SMOTE: Synthetic minority over-sampling technique;Chawla;J. Artificial Intelligence Res.,2002
4. Imbalance fault diagnosis under long-tailed distribution: Challenges, solutions and prospects;Chen;Knowl.-Based Syst.,2022
5. Multi-expert attention network with unsupervised aggregation for long-tailed fault diagnosis under speed variation;Chen;Knowl.-Based Syst.,2022