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Statistical Test for Anomaly Detections by Variational Auto-Encoders

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arxiv 2402.03724 v2 pith:3VI3QUVC submitted 2024-02-06 stat.ML cs.LG

Daiki Miwa , Tomohiro Shiraishi , Vo Nguyen Le Duy , Teruyuki Katsuoka , Ichiro Takeuchi This is my paper · ORCID
classification stat.MLcs.LG
keywords testanomalyreliabilitystatisticalvae-addetecteddetectionframework
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In this study, we consider the reliability assessment of anomaly detection (AD) using Variational Autoencoder (VAE). Over the last decade, VAE-based AD has been actively studied in various perspective, from method development to applied research. However, when the results of ADs are used in high-stakes decision-making, such as in medical diagnosis, it is necessary to ensure the reliability of the detected anomalies. In this study, we propose the VAE-AD Test as a method for quantifying the statistical reliability of VAE-based AD within the framework of statistical testing. Using the VAE-AD Test, the reliability of the anomaly regions detected by a VAE can be quantified in the form of p-values. This means that if an anomaly is declared when the p-value is below a certain threshold, it is possible to control the probability of false detection to a desired level. Since the VAE-AD Test is constructed based on a new statistical inference framework called selective inference, its validity is theoretically guaranteed in finite samples. To demonstrate the validity and effectiveness of the proposed VAE-AD Test, numerical experiments on artificial data and applications to brain image analysis are conducted.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Automatic Statistical Test for Rationally Expressible Algorithms by Selective Inference, with Applications to Feature Selection

    stat.ML 2026-08 conditional novelty 7.0 of 10

    AutoSI constructs selective-inference selection events automatically from primitive operations, enabling valid p-values for any rationally expressible algorithm, including a cross-validated lasso beyond the reach of p...

  2. Statistical Test for Saliency Maps of Graph Neural Networks via Selective Inference

    stat.ML 2025-05 conditional novelty 6.0 of 10

    A selective inference framework computes valid p-values for GNN saliency maps by conditioning on the selected salient subgraph, controlling the Type I error rate.

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