  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
https://github.com/DISL-Lab/BalanceMix.
