论文标题
PADIM：用于异常检测和本地化的补丁分布建模框架
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
论文作者
论文摘要
我们提出了一个用于补丁分布建模的新框架PADIM，以同时检测和本地化单级学习环境中的图像异常。 Padim利用验证的卷积神经网络（CNN）进行贴片嵌入，并使用多元高斯分布来获得正常类别的概率表示。它还利用了CNN不同语义级别之间的相关性，以更好地定位异常。 PADIM的表现优于MVTEC AD和STC数据集上异常检测和本地化的当前最新方法。为了匹配现实世界的视觉工业检查，我们扩展了评估协议，以评估非对准数据集上异常定位算法的性能。 Padim的最先进的表现和较低的复杂性使其成为许多工业应用的良好候选人。
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.
