AcME-AD: Accelerated Model Explanations for Anomaly Detection

arXiv:2403.01245v1 Announce Type: new
Abstract: Pursuing fast and robust interpretability in Anomaly Detection is crucial, especially due to its significance in practical applications. Traditional Anomaly Detection methods excel in outlier identification but are often black-boxes, providing scant insights into their decision-making process. This lack of transparency compromises their reliability and hampers their adoption in scenarios where comprehending the reasons behind anomaly detection is vital. At the same time, getting explanations quickly is paramount in practical scenarios. To bridge this gap, …

abstract adoption anomaly anomaly detection applications arxiv cs.lg decision detection detection methods excel identification insights interpretability making outlier practical process reliability robust significance transparency type