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arXiv · 2508.00758

Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data

Timur Sattarov·Marco Schreyer·Damian Borth
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Abstract

Anomaly detection in tabular data remains challenging due to complex feature interactions and the scarcity of anomalous examples. Denoising autoencoders rely on fixed-magnitude noise, limiting adaptability to diverse data distributions. Diffusion models introduce scheduled noise and iterative denoising, but lack explicit reconstruction mappings. We propose the Diffusion-Scheduled Denoising Autoencoder (DDAE), a framework that integrates diffusion-based noise scheduling and contrastive learning into the encoding process to improve anomaly detection. We evaluated DDAE on 57 datasets from ADBench. Our method outperforms in semi-supervised settings and achieves competitive results in unsupervised settings, improving PR-AUC by up to 65% (9%) and ROC-AUC by 16% (6%) over state-of-the-art autoencoder (diffusion) model baselines. We observed that higher noise levels benefit unsupervised training, while lower noise with linear scheduling is optimal in semi-supervised settings. These findings underscore the importance of principled noise strategies in tabular anomaly detection.

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Timur Sattarov, Marco Schreyer, Damian Borth. 2025-08-01. Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data. https://doi.org/10.1145/3711896.3736910

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