  Deep neural networks have demonstrated remarkable advancements in various
fields using large, well-annotated datasets. However, real-world data often
exhibit long-tailed distributions and label noise, significantly degrading
generalization performance. Recent studies addressing these issues have focused
on noisy sample selection methods that estimate the centroid of each class
based on high-confidence samples within each target class. The performance of
these methods is limited because they use only the training samples within each
class for class centroid estimation, making the quality of centroids
susceptible to long-tailed distributions and noisy labels. In this study, we
present a robust training framework called Distribution-aware Sample Selection
and Contrastive Learning (DaSC). Specifically, DaSC introduces a
Distribution-aware Class Centroid Estimation (DaCC) to generate enhanced class
centroids. DaCC performs weighted averaging of the features from all samples,
with weights determined based on model predictions. Additionally, we propose a
confidence-aware contrastive learning strategy to obtain balanced and robust
representations. The training samples are categorized into high-confidence and
low-confidence samples. Our method then applies Semi-supervised Balanced
Contrastive Loss (SBCL) using high-confidence samples, leveraging reliable
label information to mitigate class bias. For the low-confidence samples, our
method computes Mixup-enhanced Instance Discrimination Loss (MIDL) to improve
their representations in a self-supervised manner. Our experimental results on
CIFAR and real-world noisy-label datasets demonstrate the superior performance
of the proposed DaSC compared to previous approaches.
