Making Parametric Anomaly Detection on Tabular Data Non-Parametric Again
Hugo Thimonier Fabrice Popineau Arpad Rimmel Bich-Liên Doan
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 model learns to
reconstruct masked features of \textit{normal} samples. We test the
effectiveness of KNN-based and attention-based modules to select relevant
samples to help in the reconstruction process of the target sample. Our
experiments on a benchmark of 31 tabular datasets reveal that augmenting this
reconstruction-based anomaly detection (AD) method with non-parametric
relationships via retrieval modules may significantly boost performance.