Does Mixed Label Imbalance Matter to Minority Collapse in Imbalanced Learning?

Minority collapse, where minority classes become indistinguishable, is a significant challenge in imbalanced learning. This challenge is addressed by methods such as Mixup with class-balanced sampling. Although minority collapse has been mathematically analyzed using the layer-peeled model alongside Neural Collapse, no prior work has analyzed minority collapse under Mixup, particularly from the perspective of mixed labels. We investigate this overlooked factor and raise the question: Is mixed label balance important for alleviating minority collapse? Our analysis reveals that (i) mixed labels should be balanced, and (ii) in this setting, interpreting mixed labels as singletons is beneficial. Motivated by this analysis, we propose a Balanced Mixed Label Sampler and a Mixed-Singleton classifier, which balance mixed labels and treat them as singleton labels. Through theoretical analysis and experimental results, we highlight the importance of balancing mixed labels in imbalanced learning.