Unsupervised Anomaly Detection With Specified 7

Unsupervised anomaly detection is a technique for identifying abnormal patterns in unlabelled data. When the anomaly ratio is set to 10%, this method aims to automatically detect anomalies that constitute 10% of the total data volume. By adjusting thresholds and optimizing algorithms, it ensures the accuracy and reliability of the detection results, thereby playing a crucial role in fields such as computer vision, enhancing the robustness and security of systems.