Web: http://arxiv.org/abs/2209.10920

Sept. 23, 2022, 1:11 a.m. | Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma

cs.LG updates on arXiv.org arxiv.org

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 …

accuracy arxiv loss recall

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