Margin-aware rectified augmentation for long-tailed recognition.

• Long-tailed distribution leads to decision boundary bias in deep neural networks. • The classification performance declines under the bias caused by data imbalance. • A novel margin-aware augmentation method rectifies the biased boundary. • Large improvements on long-tailed classification benchmark with a better calibrated classifier. The long-tailed data distribution is prevalent in real world and it poses great challenge on deep neural network training. In this paper, we propose Margin-aware Rectified Augmentation (MRA) to tackle this problem. Specifically, the MRA consists of two parts. From the data perspective, we analyze that data imbalance will cause the decision boundary be biased, and we propose a novel Margin-aware Rectified mixup (MR-mixup) that adaptively rectifies the biased decision boundary. Furthermore, from the model perspective, we analyze that the imbalance will also lead to consistent 'gradient suppression' on minority class logits. Then we propose Reweighted Mutual Learning (RML) that provides extra 'soft target' as supervision signal and augments the 'encouraging gradients' on the minority classes. We conduct extensive experiments on benchmark datasets CIFAR-LT, ImageNet-LT and iNaturalist18. The results demonstrate that the proposed MRA not only achieves state-of-the-art performance, but also yields a better-calibrated prediction. [ABSTRACT FROM AUTHOR]