Visualization for Multivariate Gaussian Anomaly Detection in Images
Joao P C Bertoldo David Arrustico
This paper introduces a simplified variation of the PaDiM (Pixel-Wise Anomaly
Detection through Instance Modeling) method for anomaly detection in images,
fitting a single multivariate Gaussian (MVG) distribution to the feature
vectors extracted from a backbone convolutional neural network (CNN) and using
their Mahalanobis distance as the anomaly score. We introduce an intermediate
step in this framework by applying a whitening transformation to the feature
vectors, which enables the generation of heatmaps capable of visually
explaining the features learned by the MVG. The proposed technique is evaluated
on the MVTec-AD dataset, and the results show the importance of visual model
validation, providing insights into issues in this framework that were
otherwise invisible. The visualizations generated for this paper are publicly
available at https://doi.org/10.5281/zenodo.7937978.