We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding, and of multivariate Gaussian distributions to get a probabilistic representation of the normal class. It also exploits correlations between the different semantic levels of CNN to better localize anomalies. PaDiM outperforms current state-of-the-art approaches for both anomaly detection and localization on the MVTec AD and STC datasets. To match real-world visual industrial inspection, we extend the evaluation protocol to assess performance of anomaly localization algorithms on non-aligned dataset. The state-of-the-art performance and low complexity of PaDiM make it a good candidate for many industrial applications.

该研究提出了一种新的Patch Distribution Modeling框架（PaDiM），可以在单类学习环境下同时检测和定位图像中的异常，通过使用预训练的卷积神经网络进行补丁嵌入，并利用多元高斯分布来获得正常类的概率表示，从而优于目前MVTec AD和STC数据集上当前最先进的方法。PaDiM的性能好且复杂度低，适用于许多工业应用。

PaDiM: 一种用于异常检测和定位的Patch分布建模框架