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cs.AI2024
Relation Modeling and Distillation for Learning with Noisy Labels
Xiaming Che, Junlin Zhang, Zhuang Qi +1
Learning with noisy labels has become an effective strategy for enhancing the robustness of models, which enables models to better tolerate inaccurate data. Existing methods either…
cs.AI2024
Federated Cross-Training Learners for Robust Generalization under Data Heterogeneity
Zhuang Qi, Lei Meng, Ruohan Zhang +5
Federated learning benefits from cross-training strategies, which enables models to train on data from distinct sources to improve generalization capability. However, due to inhere…