PatchProto Networks for Few-shot Visual Anomaly Classification
Jian Wang Yue Zhuo
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.