mlsanityData to 14 Oct 2026

Statistical Test for Anomaly Detections by Variational Auto-Encoders

arXiv 2402.03724 ↗First posted 6 Feb 2024stat.ML cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 23 of its 37 references that are in the map. First posted on arXiv 6 Feb 2024.

Builds on

23 of 37 references.

References · 23

  1. 2019Deep Learning for Anomaly Detection: A Surveycited by 358
  2. 2020Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Studycited by 147
  3. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
  4. 2013Exact post-selection inference, with application to the lassocited by 91
  5. 2018Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoderscited by 83
  6. 2019Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmcited by 77
  7. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 68
  8. 2014Exact Post-Selection Inference for Sequential Regression Procedurescited by 48
  9. 2020Iterative energy-based projection on a normal data manifold for anomaly localizationcited by 47
  10. 2020Selective Inference for Hierarchical Clusteringcited by 39
  11. 2017Valid Inference Corrected for Outlier Removalcited by 26
  12. 2020Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programmingcited by 25
11 more
  1. 2021More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programmingcited by 25
  2. 2019Computing Valid p-values for Image Segmentation by Selective Inferencecited by 24
  3. 2020Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inferencecited by 21
  4. 2018Anomaly Detection for Skin Disease Images Using Variational Autoencodercited by 20
  5. 2014Selecting the number of principal components: estimation of the true rank of a noisy matrixcited by 18
  6. 2014A significance test for forward stepwise model selectioncited by 17
  7. 2015Selective Sequential Model Selectioncited by 17
  8. 2023Valid P-Value for Deep Learning-Driven Salient Regioncited by 17
  9. 2019adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detectioncited by 11
  10. 2023CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inferencecited by 10
  11. 2024Statistical Test for Attention Map in Vision Transformercited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 3.

7 papers.

  1. 2025Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inferencecited by 3
  2. 2024Statistical Test for Diffusion-Based Anomaly Localization via Selective Inferencecited by 1
  3. 2025Statistical Test for Saliency Maps of Graph Neural Networks via Selective Inferencecited by 1
  4. 2026Selective Inference for Deep Clustering in Latent Spacescited by 1
  5. 2024Statistical testing on generative AI anomaly detection tools in Alzheimer's Disease diagnosiscited by 0
  6. 2026A statistical perspective on transformers for small longitudinal cohort datacited by 0
  7. 2026Automatic Statistical Test for Rationally Expressible Algorithms by Selective Inference, with Applications to Feature Selectioncited by 0