  Class imbalance problems frequently occur in real-world tasks, and
conventional deep learning algorithms are well known for performance
degradation on imbalanced training datasets. To mitigate this problem, many
approaches have aimed to balance among given classes by re-weighting or
re-sampling training samples. These re-balancing methods increase the impact of
minority classes and reduce the influence of majority classes on the output of
models. However, the extracted representations may be of poor quality owing to
the limited number of minority samples. To handle this restriction, several
methods have been developed that increase the representations of minority
samples by leveraging the features of the majority samples. Despite extensive
recent studies, no deep analysis has been conducted on determination of classes
to be augmented and strength of augmentation has been conducted. In this study,
we first investigate the correlation between the degree of augmentation and
class-wise performance, and find that the proper degree of augmentation must be
allocated for each class to mitigate class imbalance problems. Motivated by
this finding, we propose a simple and efficient novel curriculum, which is
designed to find the appropriate per-class strength of data augmentation,
called CUDA: CUrriculum of Data Augmentation for long-tailed recognition. CUDA
can simply be integrated into existing long-tailed recognition methods. We
present the results of experiments showing that CUDA effectively achieves
better generalization performance compared to the state-of-the-art method on
various imbalanced datasets such as CIFAR-100-LT, ImageNet-LT, and iNaturalist
2018.
