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 this https URL

本文介绍了 PaDiM 方法的简化，使用单变量高斯分布拟合从卷积神经网络中提取的特征向量，将它们的马氏距离作为异常值评分，并通过执行白化转换生成能够可视化 MVG 学到特征的热图，MVTec-AD 数据集的实验结果表明，视觉模型验证的重要性，提供了这个框架中，本来无法察觉的问题的见解。本文生成的可视化结果可以通过公开链接获得。