Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

We design two novel methods to improve performance in such scenarios. First, we propose a theoretically-principled label-distribution-aware margin (LDAM) loss motivated by minimizing a margin-based generalization bound.