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.

翻译：类别不平衡分布广泛存在于实际工程中。然而，旨在最小化误差的主流优化算法在面对极端类别不平衡时，会使深度学习模型陷入次优解。这严重损害了分类精度，尤其对少数类别影响显著。其根本原因在于分类器权重的梯度在不同类别分量间存在不平衡。本文提出吸引-排斥平衡损失（ARB-Loss）以平衡梯度的不同分量。我们在大规模分类与分割数据集上开展实验，仅需单阶段训练即可达到当前最优性能，而无需像现有先进方法那样采用两阶段学习。