  Class imbalance distribution widely exists in real-world engineering.
However, the mainstream optimization algorithms that seek to minimize error
will trap the deep learning model in sub-optimums when facing extreme class
imbalance. It seriously harms the classification precision, especially on the
minor classes. The essential reason is that the gradients of the classifier
weights are imbalanced among the components from different classes. In this
paper, we propose Attraction-Repulsion-Balanced Loss (ARB-Loss) to balance the
different components of the gradients. We perform experiments on the
large-scale classification and segmentation datasets and our ARB-Loss can
achieve state-of-the-art performance via only one-stage training instead of
2-stage learning like nowadays SOTA works.
