mlsanityData to 7 Oct 2026

Statistical Inference for Autoencoder-based Anomaly Detection after Representation Learning-based Domain Adaptation

arXiv 2508.07049 ↗First posted 9 Aug 2025stat.ML cs.LGcited by 2

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Abstract (arXiv)

Anomaly detection (AD) plays a vital role across a wide range of domains, but its performance might deteriorate when applied to target domains with limited data. Domain Adaptation (DA) offers a solution by transferring knowledge from a related source domain with abundant data. However, this adaptation process can introduce additional uncertainty, making it difficult to draw statistically valid conclusions from AD results. In this paper, we propose STAND-DA -- a novel framework for statistically rigorous Autoencoder-based AD after Representation Learning-based DA. Built on the Selective Inference (SI) framework, STAND-DA computes valid p-values for detected anomalies and rigorously controls the false positive rate below a pre-specified level α (e.g., 0.05). To address the computational challenges of applying SI to deep learning models, we develop the GPU-accelerated SI implementation, significantly enhancing both scalability and runtime performance. This advancement makes SI practically feasible for modern, large-scale deep architectures. Extensive experiments on synthetic and real-world datasets validate the theoretical results and computational efficiency of the proposed STAND-DA method.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 27 of its 48 references that are in the map. First posted on arXiv 9 Aug 2025.

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27 of 48 references.

References · 27

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15 more
  1. 2020Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inferencecited by 21
  2. 2022Selective inference for k-means clusteringcited by 21
  3. 2018Post-Selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Datacited by 19
  4. 2014A significance test for forward stepwise model selectioncited by 17
  5. 2023Valid P-Value for Deep Learning-Driven Salient Regioncited by 17
  6. 2015Selective Sequential Model Selectioncited by 16
  7. 2021Fast and More Powerful Selective Inference for Sparse High-order Interaction Modelcited by 15
  8. 2021Tree-Values: selective inference for regression treescited by 15
  9. 2016Post Selection Inference with Kernelscited by 13
  10. 2017Selective Inference for Change Point Detection in Multi-dimensional Sequencescited by 11
  11. 2021Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inferencecited by 10
  12. 2023CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inferencecited by 10
  13. 2021Exact Statistical Inference for the Wasserstein Distance by Selective Inferencecited by 7
  14. 2020More Powerful and General Selective Inference for Stepwise Feature Selection using the Homotopy Continuation Approachcited by 6
  15. 2025si4onnx: A Python package for Selective Inference in Deep Learning Modelscited by 4

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Citing papers per year: 2026: 2.

2 papers.

  1. 2026GCA: Global Centroid Alignment in Federated Learningcited by 0
  2. 2026Post-Anomaly Detection Inference for Deep SVDDcited by 0