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.

翻译：我们提出了一种用于不平衡分类的新型损失函数。LDAM损失通过最小化基于间隔的泛化界，被广泛应用于类别不平衡的图像分类中。尽管使用LDAM损失可以为少数类获得大间隔，为多数类获得小间隔，但原始softmax交叉熵损失中所包含的大间隔的相关性尚未明确。在本研究中，我们利用基于softplus函数的大间隔softmax交叉熵损失概念重新推导LDAM损失的公式，并确认LDAM损失包含比softmax交叉熵损失更宽的大间隔。此外，我们提出了一种新型的扩大大间隔（ELM）损失，该损失能进一步拓宽LDAM损失的大间隔。ELM损失在LDAM损失中使用的基本间隔基础上，进一步为错误类别的最大logit利用大间隔。通过在类别不平衡的CIFAR数据集和具有长尾分布的大规模数据集上进行的实验，我们证实与LDAM损失和传统不平衡分类损失相比，ELM损失的分类准确率有显著提高。