Enlarged Large Margin Loss for Imbalanced Classification
Sota Kato Kazuhiro Hotta
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.