Multi-label classification poses challenges due to imbalanced and noisy
labels in training data. We propose a unified data augmentation method, named
BalanceMix, to address these challenges. Our approach includes two samplers for
imbalanced labels, generating minority-augmented instances with high diversity.
It also refines multi-labels at the label-wise granularity, categorizing noisy
labels as clean, re-labeled, or ambiguous for robust optimization. Extensive
experiments on three benchmark datasets demonstrate that BalanceMix outperforms
existing state-of-the-art methods. We release the code at
this https URL

我们提出了一种统一的数据增强方法 BalanceMix，用于解决多标签分类中的类别不平衡和噪声问题。我们的方法包括两个样本策略，用于生成具有高多样性的少数类增强样本。它还在标签粒度上对多标签进行细分，将噪声标签归类为清晰、重新标记或不确定，以实现鲁棒优化。在三个基准数据集上进行的大量实验表明，BalanceMix 优于现有的最先进方法。我们在此 https 的网址上发布了代码。