We propose a novel loss function for imbalanced classification. LDAM loss, which minimizes a margin-based generalization bound, is widely utilized for class-imbalanced image classification. Although, by using LDAM loss, it is possible to obtain large margins for the minority classes and small margins for the majority classes, the relevance to a large margin, which is included in the original softmax cross entropy loss, is not be clarified yet. In this study, we reconvert the formula of LDAM loss using the concept of the large margin softmax cross entropy loss based on the softplus function and confirm that LDAM loss includes a wider large margin than softmax cross entropy loss. Furthermore, we propose a novel Enlarged Large Margin (ELM) loss, which can further widen the large margin of LDAM loss. ELM loss utilizes the large margin for the maximum logit of the incorrect class in addition to the basic margin used in LDAM loss. Through experiments conducted on imbalanced CIFAR datasets and large-scale datasets with long-tailed distribution, we confirmed that classification accuracy was much improved compared with LDAM loss and conventional losses for imbalanced classification.

本文提出了一种新的针对非均衡分类的损失函数LDAM loss，同时提出了一种扩大较大边界的ELM loss，并通过对非均衡CIFAR数据集和长尾分布大规模数据集的实验验证，证明了相较于标准的LDAM loss和传统的非均衡分类损失函数，该方法能够显著提高分类准确度。

不平衡分类的扩展大间隔损失