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Label noise is a critical factor that degrades the generalization performanceof deep neural networks, thus leading to severe issues in real-world problems.Existing studies have employed strategies based on either loss or uncertaintyto address noisy labels, and ironically some strategies contradict each other:emphasizing or discarding uncertain samples or concentrating on high or lowloss samples. To elucidate how opposing strategies can enhance modelperformance and offer insights into training with noisy labels, we presentanalytical results on how loss and uncertainty values of samples changethroughout the training process. From the in-depth analysis, we design a newrobust training method that emphasizes clean and informative samples, whileminimizing the influence of noise using both loss and uncertainty. Wedemonstrate the effectiveness of our method with extensive experiments onsynthetic and real-world datasets for various deep learning models. The resultsshow that our method significantly outperforms other state-of-the-art methodsand can be used generally regardless of neural network architectures.

Which Strategies Matter for Noisy Label Classification? Insight into Loss and Uncertainty