  A learning algorithm referred to as Maximum Margin (MM) is proposed for
considering the class-imbalance data learning issue: the trained model tends to
predict the majority of classes rather than the minority ones. That is,
underfitting for minority classes seems to be one of the challenges of
generalization. For a good generalization of the minority classes, we design a
new Maximum Margin (MM) loss function, motivated by minimizing a margin-based
generalization bound through the shifting decision bound. The
theoretically-principled label-distribution-aware margin (LDAM) loss was
successfully applied with prior strategies such as re-weighting or re-sampling
along with the effective training schedule. However, they did not investigate
the maximum margin loss function yet. In this study, we investigate the
performances of two types of hard maximum margin-based decision boundary shift
with LDAM's training schedule on artificially imbalanced CIFAR-10/100 for fair
comparisons and effectiveness.
