Making Parametric Anomaly Detection on Tabular Data Non-Parametric Again

Deep learning for tabular data has garnered increasing attention in recent years, yet employing deep models for structured data remains challenging. While these models excel with unstructured data, their efficacy with structured data has been limited. Recent research has introduced retrieval-augmented models to address this gap, demonstrating promising results in supervised tasks such as classification and regression. In this work, we investigate using retrieval-augmented models for anomaly detection on tabular data. We propose a reconstruction-based approach in which a transformer …

anomaly anomaly detection attention cs.lg data deep learning detection excel gap making non-parametric parametric research retrieval retrieval-augmented structured data tabular tabular data unstructured unstructured data