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.

翻译：本文介绍了PaDiM（通过实例建模的逐像素异常检测）方法的一种简化变体，用于图像异常检测。该方法将从主干卷积神经网络（CNN）提取的特征向量拟合至单个多元高斯（MVG）分布，并以特征向量的马氏距离作为异常分数。我们在该框架中引入中间步骤，对特征向量应用白化变换，从而能够生成热力图以直观解释MVG所学习到的特征。所提技术在MVTec-AD数据集上进行了评估，结果表明视觉模型验证具有重要意义，可揭示该框架中原本不可见的问题。本文生成的可视化结果已在https://doi.org/10.5281/zenodo.7937978 上公开。