OEST: Outlier Exposure by Simple Transformations for Out-of-Distribution Detection

Yifan Wu, Songmin Dai, Dengye Pan, Xiaoqiang Li

Abstract: Although the previous works for out-of-distribution(OOD) detection have achieved great improvements, they are still highly dependent on the specific selection of the outliers from external datasets or that transformed by certain data augmentations, and hence cannot be applied in a wide range of domains. To solve this problem, in this paper, we propose a simple, yet effective method called Outlier Exposure by Simple Transformations (OEST), which aims at exposing the outliers by the composition of several simple transformations of data augmentations via energy score. In addition, our training scheme can make full use of nearly all considered data augmentations in previous works, even though some of them are generally regarded as useless. And we also find that, for simple data augmentation, our training scheme is less time-consuming and better in performance than relative works. Furthermore, our experiments validate that our method outperforms the state-of-the-art methods.