  Label noise is a critical factor that degrades the generalization performance
of deep neural networks, thus leading to severe issues in real-world problems.
Existing studies have employed strategies based on either loss or uncertainty
to address noisy labels, and ironically some strategies contradict each other:
emphasizing or discarding uncertain samples or concentrating on high or low
loss samples. To elucidate how opposing strategies can enhance model
performance and offer insights into training with noisy labels, we present
analytical results on how loss and uncertainty values of samples change
throughout the training process. From the in-depth analysis, we design a new
robust training method that emphasizes clean and informative samples, while
minimizing the influence of noise using both loss and uncertainty. We
demonstrate the effectiveness of our method with extensive experiments on
synthetic and real-world datasets for various deep learning models. The results
show that our method significantly outperforms other state-of-the-art methods
and can be used generally regardless of neural network architectures.
