Multivariate Beta Normality Scores Approach for Deep Anomaly Detection in Images Using Transformations

In this work, we propose a novel anomaly detection approach in images based on normality scores using transformations. By applying various transformations to the input image such as rotation and flipping, we train a classifier to predict the transformation label applied to the images. Then, we represent the output of the classifier by a softmax vector. Thanks to the flexibility of multivariate Beta in fitting the data compared to other conventional distributions such as the Dirichlet distribution, we approximate the softmax vector by this general form of the Beta distribution to construct the normality scores. Moreover, we use the Maximum Likelihood to estimate the parameters of the proposed distribution. To show the power and the effectiveness of our approach, we conduct experiments of detecting anomalies in various public datasets. Furthermore, the proposed method is compared with state-of-the-art techniques and results demonstrate its superiority in terms of Area Under Receiver Operating characteristics (AUROC).