  In the field of out-of-distribution (OOD) detection, a previous method that
use auxiliary data as OOD data has shown promising performance. However, the
method provides an equal loss to all auxiliary data to differentiate them from
inliers. However, based on our observation, in various tasks, there is a
general imbalance in the distribution of the auxiliary OOD data across classes.
We propose a balanced energy regularization loss that is simple but generally
effective for a variety of tasks. Our balanced energy regularization loss
utilizes class-wise different prior probabilities for auxiliary data to address
the class imbalance in OOD data. The main concept is to regularize auxiliary
samples from majority classes, more heavily than those from minority classes.
Our approach performs better for OOD detection in semantic segmentation,
long-tailed image classification, and image classification than the prior
energy regularization loss. Furthermore, our approach achieves state-of-the-art
performance in two tasks: OOD detection in semantic segmentation and
long-tailed image classification. Code is available at
https://github.com/hyunjunChhoi/Balanced_Energy.
