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Anomaly detection is vital in many domains, such as finance, healthcare, andcybersecurity. In this paper, we propose a novel deep anomaly detection methodfor tabular data that leverages Non-Parametric Transformers (NPTs), a modelinitially proposed for supervised tasks, to capture both feature-feature andsample-sample dependencies. In a reconstruction-based framework, we train theNPT to reconstruct masked features of normal samples. In a non-parametricfashion, we leverage the whole training set during inference and use themodel's ability to reconstruct the masked features to generate an anomalyscore. To the best of our knowledge, this is the first work to successfullycombine feature-feature and sample-sample dependencies for anomaly detection ontabular datasets. Through extensive experiments on 31 benchmark tabulardatasets, we demonstrate that our method achieves state-of-the-art performance,outperforming existing methods by 2.4% and 1.2% in terms of F1-score and AUROC,respectively. Our ablation study provides evidence that modeling both types ofdependencies is crucial for anomaly detection on tabular data.

Beyond Individual Input for Deep Anomaly Detection on Tabular Data