  Label noise is ubiquitous in real-world scenarios, posing a practical
challenge to supervised models due to its effect in hurting the generalization
performance of deep neural networks. Existing methods primarily employ the
sample selection paradigm and usually rely on dataset-dependent prior knowledge
(\eg, a pre-defined threshold) to cope with label noise, inevitably degrading
the adaptivity. Moreover, existing methods tend to neglect the class balance in
selecting samples, leading to biased model performance. To this end, we propose
a simple yet effective approach named \textbf{SED} to deal with label noise in
a \textbf{S}elf-adaptiv\textbf{E} and class-balance\textbf{D} manner.
Specifically, we first design a novel sample selection strategy to empower
self-adaptivity and class balance when identifying clean and noisy data. A
mean-teacher model is then employed to correct labels of noisy samples.
Subsequently, we propose a self-adaptive and class-balanced sample re-weighting
mechanism to assign different weights to detected noisy samples. Finally, we
additionally employ consistency regularization on selected clean samples to
improve model generalization performance. Extensive experimental results on
synthetic and real-world datasets demonstrate the effectiveness and superiority
of our proposed method. The source code has been made available at
https://github.com/NUST-Machine-Intelligence-Laboratory/SED.
