mlsanityData to 13 Oct 2026

Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift

arXiv 2503.14910 ↗First posted 19 Mar 2025cs.CVcited by 5

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Abstract (arXiv)

Anomaly detection plays a crucial role in quality control for industrial applications. However, ensuring robustness under unseen domain shifts such as lighting variations or sensor drift remains a significant challenge. Existing methods attempt to address domain shifts by training generalizable models but often rely on prior knowledge of target distributions and can hardly generalise to backbones designed for other data modalities. To overcome these limitations, we build upon memory-bank-based anomaly detection methods, optimizing a robust Sinkhorn distance on limited target training data to enhance generalization to unseen target domains. We evaluate the effectiveness on both 2D and 3D anomaly detection benchmarks with simulated distribution shifts. Our proposed method demonstrates superior results compared with state-of-the-art anomaly detection and domain adaptation methods.

Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 32 of its 43 references that are in the map. First posted on arXiv 19 Mar 2025.

Builds on

32 of 43 references.

References · 32

  1. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,862
  2. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,295
  3. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,909
  4. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 774
  5. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 511
  6. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 420
  7. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  8. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  9. 2017Joint Distribution Optimal Transportation for Domain Adaptationcited by 281
  10. 2022A Unified Model for Multi-class Anomaly Detectioncited by 268
  11. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  12. 2019Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptationcited by 235
20 more
  1. 2018DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptationcited by 213
  2. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 177
  3. 2020Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfercited by 173
  4. 2020Unsupervised Domain Adaptation via Structurally Regularized Deep Clusteringcited by 140
  5. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 139
  6. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  7. 2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
  8. 2020Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptationcited by 86
  9. 2023Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcorecited by 75
  10. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  11. 2018Banach Wasserstein GANcited by 56
  12. 2024SoftPatch: Unsupervised Anomaly Detection with Noisy Datacited by 55
  13. 2020Source-Relaxed Domain Adaptation for Image Segmentationcited by 52
  14. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  15. 2020Outlier-Robust Optimal Transportcited by 45
  16. 2022Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clusteringcited by 44
  17. 2023Anomaly Detection under Distribution Shiftcited by 35
  18. 2023Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Trainingcited by 19
  19. 2024DMAD: Dual Memory Bank for Real-World Anomaly Detectioncited by 5
  20. 2024ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shiftcited by 5

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detectioncited by 4
  2. 2026SPARC: Subspace Position-Aware Robust Few-Shot Calibration for Distribution-Shifted Industrial Anomaly Detectioncited by 1
  3. 2026Test-Time Adaptation for Anomaly Segmentation via Topology-Aware Optimal Transport Chainingcited by 0
  4. 2026LogiCo: A Unified Framework for Logical and Structural Anomaly Detectioncited by 0
  5. 2026UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Constructioncited by 0