Being able to spot defective parts is a critical component in large-scale
industrial manufacturing. A particular challenge that we address in this work
is the cold-start problem: fit a model using nominal (non-defective) example
images only. While handcrafted solutions per class are possible, the goal is to
build systems that work well simultaneously on many different tasks
automatically. The best performing approaches combine embeddings from ImageNet
models with an outlier detection model. In this paper, we extend on this line
of work and propose \textbf{PatchCore}, which uses a maximally representative
memory bank of nominal patch-features. PatchCore offers competitive inference
times while achieving state-of-the-art performance for both detection and
localization. On the challenging, widely used MVTec AD benchmark PatchCore
achieves an image-level anomaly detection AUROC score of up to $99.6\%$, more
than halving the error compared to the next best competitor. We further report
competitive results on two additional datasets and also find competitive
results in the few samples regime.\freefootnote{$^*$ Work done during a
research internship at Amazon AWS.} Code:
github.com/amazon-research/patchcore-inspection.

本文提出了一种名为 PatchCore 的方法，它使用最具代表性的内存库来实现商标标准化的图像检测和本地化。PatchCore 在 MVTec AD 挑战赛中实现了高达 99.6％的图像级异常检测 AUROC 分数，并在其他两个数据集中取得了竞争性结果。