  Learning with noisy labels has gained increasing attention because the
inevitable imperfect labels in real-world scenarios can substantially hurt the
deep model performance. Recent studies tend to regard low-loss samples as clean
ones and discard high-loss ones to alleviate the negative impact of noisy
labels. However, real-world datasets contain not only noisy labels but also
class imbalance. The imbalance issue is prone to causing failure in the
loss-based sample selection since the under-learning of tail classes also leans
to produce high losses. To this end, we propose a simple yet effective method
to address noisy labels in imbalanced datasets. Specifically, we propose
Class-Balance-based sample Selection (CBS) to prevent the tail class samples
from being neglected during training. We propose Confidence-based Sample
Augmentation (CSA) for the chosen clean samples to enhance their reliability in
the training process. To exploit selected noisy samples, we resort to
prediction history to rectify labels of noisy samples. Moreover, we introduce
the Average Confidence Margin (ACM) metric to measure the quality of corrected
labels by leveraging the model's evolving training dynamics, thereby ensuring
that low-quality corrected noisy samples are appropriately masked out. Lastly,
consistency regularization is imposed on filtered label-corrected noisy samples
to boost model performance. Comprehensive experimental results on synthetic and
real-world datasets demonstrate the effectiveness and superiority of our
proposed method, especially in imbalanced scenarios. Comprehensive experimental
results on synthetic and real-world datasets demonstrate the effectiveness and
superiority of our proposed method, especially in imbalanced scenarios.
