  Noisy labels, which are common in real-world datasets, can significantly
impair the training of deep learning models. However, recent adversarial
noise-combating methods overlook the long-tailed distribution of real data,
which can significantly harm the effect of denoising strategies. Meanwhile, the
mismanagement of noisy labels further compromises the model's ability to handle
long-tailed data. To tackle this issue, we propose a novel approach to manage
data characterized by both long-tailed distributions and noisy labels. First,
we introduce a loss-distance cross-selection module, which integrates class
predictions and feature distributions to filter clean samples, effectively
addressing uncertainties introduced by noisy labels and long-tailed
distributions. Subsequently, we employ optimal transport strategies to generate
pseudo-labels for the noise set in a semi-supervised training manner, enhancing
pseudo-label quality while mitigating the effects of sample scarcity caused by
the long-tailed distribution. We conduct experiments on both synthetic and
real-world datasets, and the comprehensive experimental results demonstrate
that our method surpasses current state-of-the-art methods. Our code will be
available in the future.
