  Mixup is a popular data augmentation method, with many variants subsequently
proposed. These methods mainly create new examples via convex combination of
random data pairs and their corresponding one-hot labels. However, most of them
adhere to a random sampling and mixing strategy, without considering the
frequency of label occurrence in the mixing process. When applying mixup to
long-tailed data, a label suppression issue arises, where the frequency of
label occurrence for each class is imbalanced and most of the new examples will
be completely or partially assigned with head labels. The suppression effect
may further aggravate the problem of data imbalance and lead to a poor
performance on tail classes. To address this problem, we propose
Label-Occurrence-Balanced Mixup to augment data while keeping the label
occurrence for each class statistically balanced. In a word, we employ two
independent class-balanced samplers to select data pairs and mix them to
generate new data. We test our method on several long-tailed vision and sound
recognition benchmarks. Experimental results show that our method significantly
promotes the adaptability of mixup method to imbalanced data and achieves
superior performance compared with state-of-the-art long-tailed learning
methods.
