Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generation

ArXi:2604.18266v1 Announce Type: new Identifying anomalous instances in tabular data is essential for improving data reliability and maintaining system stability. Due to the scarcity of ground-truth anomaly labels, existing methods mainly rely on unsupervised anomaly detection models, or exploit a small number of labeled anomalies to facilitate detection via sample generation or contrastive learning.