DAMEL: Dual-Axis Multi-Expert Learning for Class-Imbalanced Learning 事件

DAMEL: Dual-Axis Multi-Expert Learning for Class-Imbalanced Learning arXiv:2605.30135v1 Announce Type: cross Abstract: Various algorithms have been proposed to address the challenges posed by class-imbalanced learning from real-world data with long-tailed distributions. While these algorithms reduce prediction bias through rebalancing techniques, they often introduce increased prediction variance as a trade-off. Several multi-expert learning algorithms aim to address this variance but involve c