Enlarged Large Margin Loss for Imbalanced Classification
This work addresses class imbalance in image classification, offering a method that enhances performance for minority classes, though it appears incremental as it builds directly on prior LDAM loss.
The authors tackled the problem of imbalanced classification by proposing a novel Enlarged Large Margin (ELM) loss function, which improved classification accuracy on imbalanced CIFAR and large-scale long-tailed datasets compared to existing methods like LDAM loss.
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.