Anomaly Classification

Anomaly Classification is a technique in the field of computer vision that aims to identify and classify abnormal patterns, designed to detect anomalous samples that deviate from the norm within a large set of normal data. This technology trains models to learn the characteristics of normal samples, thereby achieving efficient recognition and classification of anomalies. Anomaly Classification is widely applied in areas such as industrial quality inspection, medical diagnosis, and security monitoring, effectively enhancing the detection accuracy and response speed of abnormal events, reducing false alarm rates and missed detection rates, and improving the overall reliability and safety of systems.