Making Parametric Anomaly Detection on Tabular Data Non-Parametric Again. (arXiv:2401.17052v1 [cs.LG])

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 arxiv 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