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cs.LG2024
Dataset Distillers Are Good Label Denoisers In the Wild
Lechao Cheng, Kaifeng Chen, Jiyang Li +3
Learning from noisy data has become essential for adapting deep learning models to real-world applications. Traditional methods often involve first evaluating the noise and then ap…
cs.LG2024
FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation
Yuting Ma, Lechao Cheng, Yaxiong Wang +3
Federated learning (FL) is a popular privacy-preserving paradigm that enables distributed clients to collaboratively train models with a central server while keeping raw data local…