Cross-to-merge training with class balance strategy for learning with noisy labels

Qian Zhang, Yi Zhu, Ming Yang, Ge Jin, Yingwen Zhu, Qiu Chen

Abstract: Highlights•A pioneering framework called Cross-to-Merge Training (C2MT) is proposed for learning with noisy labels (LNL).•We are the first to perform parameter aggregation on LNL after cross-training.•We introduce a novel class-balancing strategy, the Median Balance Strategy (MBS), which proves useful for sample selection.•C2MT demonstrates robustness with respect to hyper-parameters and network architectures.•Comprehensive experimental results substantiate the advantages of C2MT.