Anomaly Contamination

Anomaly contamination, the presence of unlabeled anomalies within training data, significantly hinders the accuracy of anomaly detection models across various applications, from system monitoring to manufacturing quality control. Current research focuses on developing robust algorithms, including semi-supervised and contrastive learning methods, that mitigate the negative impact of contaminated data by leveraging hierarchical relationships between data points or incorporating continuous supervisory signals. These advancements aim to improve the reliability and efficiency of anomaly detection systems, leading to more accurate identification of deviations from normal behavior in diverse fields.