Visual anomaly detection focuses on identifying deviations from the norm within visual data. This process involves training algorithms to recognize patterns or features within images or videos and then flagging any instances that deviate from these learned patterns. The anomalies detected could be anything from defects in manufactured goods to unusual activity in surveillance footage. The core idea is to automate the inspection process, allowing for quicker and more accurate identification of issues that might be missed by human observers.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/testing-fomo-ad-and-other-anomaly-detection-models