Pseudo-Outlier Synthesis Using Q-Gaussian Distributions for Out-of-Distribution Detection

Abstract

Out-of-distribution (OOD) detection, which aims to determine whether an input is outside the training data distribution or not, is an indispensable task in many computer vision applications. In many of the previous studies on OOD detection for image classification, the class-conditional distribution of visual features is assumed to be a Gaussian. However, this may not be a reasonable assumption because unseen outliers do not always follow a Gaussian distribution. In this study, we investigated the potential effects of non-Gaussian distributions by using an OOD detection method based on Tsallis statistics, in which the family of q-Gaussian distributions involving short- and long-tail distributions are used for synthesizing pseudo outlier features for improving the effectiveness of training. In experiments on six image classification datasets, we show that the proposed method achieves good results in the comparison method. In addition, we find that samples with a smaller hem than the Gaussian distribution by all datasets by ablation studies of the tail of the distribution improve the performance of OOD detection.