The visual anomaly diagnosis can automatically analyze the defective
products, which has been widely applied in industrial quality inspection. The
anomaly classification can classify the defective products into different
categories. However, the anomaly samples are hard to access in practice, which
impedes the training of canonical machine learning models. This paper studies a
practical issue that anomaly samples for training are extremely scarce, i.e.,
few-shot learning (FSL). Utilizing the sufficient normal samples, we propose
PatchProto networks for few-shot anomaly classification. Different from
classical FSL methods, PatchProto networks only extract CNN features of
defective regions of interest, which serves as the prototypes for few-shot
learning. Compared with basic few-shot classifier, the experiment results on
MVTec-AD dataset show PatchProto networks significantly improve the few-shot
anomaly classification accuracy.

本论文研究了少样本学习（FSL）的实际问题，通过利用充足的正常样本，提出了 PatchProto 网络用于少样本异常分类。与传统方法不同，PatchProto 网络仅提取感兴趣区域的缺陷 CNN 特征，并将其作为少样本学习的原型。实验结果显示 PatchProto 网络显著提高了少样本异常分类的准确性。