PatchProto Networks for Few-shot Visual Anomaly Classification
This work addresses a practical issue in industrial quality inspection by enabling anomaly classification with limited anomaly data, though it is incremental as it builds on existing few-shot learning methods.
The paper tackles the problem of few-shot anomaly classification in industrial quality inspection, where anomaly samples are scarce, by proposing PatchProto networks that extract CNN features from defective regions as prototypes, resulting in significant accuracy improvements on the MVTec-AD dataset.
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.