  In real-world applications of multi-class classification models,
misclassification in an important class (e.g., stop sign) can be significantly
more harmful than in other classes (e.g., speed limit). In this paper, we
propose a loss function that can improve the recall of an important class while
maintaining the same level of accuracy as the case using cross-entropy loss.
For our purpose, we need to make the separation of the important class better
than the other classes. However, existing methods that give a class-sensitive
penalty for cross-entropy loss do not improve the separation. On the other
hand, the method that gives a margin to the angle between the feature vectors
and the weight vectors of the last fully connected layer corresponding to each
feature can improve the separation. Therefore, we propose a loss function that
can improve the separation of the important class by setting the margin only
for the important class, called Class-sensitive Additive Angular Margin Loss
(CAMRI Loss). CAMRI loss is expected to reduce the variance of angles between
features and weights of the important class relative to other classes due to
the margin around the important class in the feature space by adding a penalty
to the angle. In addition, concentrating the penalty only on the important
classes hardly sacrifices the separation of the other classes. Experiments on
CIFAR-10, GTSRB, and AwA2 showed that the proposed method could improve up to
9% recall improvement on cross-entropy loss without sacrificing accuracy.
