Unsupervised Anomaly Detection With Specified 4

Unsupervised anomaly detection refers to the technique of identifying data points that significantly deviate from normal patterns within an unlabelled dataset. When setting the anomaly ratio to 20%, this method aims to automatically discover 20% of the samples that are anomalies from the data, without prior knowledge of which ones are abnormal. This technology has significant application value in computer vision, effectively enhancing the accuracy and robustness of image and video analysis, and is applicable to scenarios such as fault detection and intrusion monitoring.