Interpretable Maximum Margin Deep Anomaly Detection

ArXi:2603.07073v1 Announce Type: new Anomaly detection is a crucial machine-learning task with wide-ranging applications. Deep Vector Data Description (Deep SVDD) is a prominent deep one-class method, but it is vulnerable to hypersphere collapse, often relies on heuristic choices for hypersphere parameters, and provides limited interpretability. To address these issues, we propose Interpretable Maximum Margin Deep Anomaly Detection (IMD-AD), which leverages a small set of labeled anomalies and a maximum margin objective to stabilize.