PaDiM：异常检测和本地化的补丁分发建模框架

我们提供了一种新的补丁分发建模框架PaDiM，可以在一类学习环境中同时检测和定位图像中的异常。PaDiM利用预训练卷积神经网络（CNN）进行补丁嵌入，并使用多元高斯分布来获得正态类的概率表示。..

PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

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.