mlsanityData to 14 Oct 2026

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection

arXiv 2502.20981 ↗First posted 28 Feb 2025cs.CVcited by 10

Not on the map. Why?

Abstract (arXiv)

In Open-set Supervised Anomaly Detection (OSAD), the existing methods typically generate pseudo anomalies to compensate for the scarcity of observed anomaly samples, while overlooking critical priors of normal samples, leading to less effective discriminative boundaries. To address this issue, we propose a Distribution Prototype Diffusion Learning (DPDL) method aimed at enclosing normal samples within a compact and discriminative distribution space. Specifically, we construct multiple learnable Gaussian prototypes to create a latent representation space for abundant and diverse normal samples and learn a Schr"odinger bridge to facilitate a diffusive transition toward these prototypes for normal samples while steering anomaly samples away. Moreover, to enhance inter-sample separation, we design a dispersion feature learning way in hyperspherical space, which benefits the identification of out-of-distribution anomalies. Experimental results demonstrate the effectiveness and superiority of our proposed DPDL, achieving state-of-the-art performance on 9 public datasets.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 33 of its 46 references that are in the map. First posted on arXiv 28 Feb 2025.

Builds on

33 of 46 references.

References · 33

  1. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  2. 2021Diffusion Schr"odinger Bridge with Applications to Score-Based Generative Modelingcited by 602
  3. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  4. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  5. 2013A survey of the Schr"odinger problem and some of its connections with optimal transportcited by 292
  6. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  7. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 271
  8. 2019Deep Anomaly Detection with Deviation Networkscited by 153
  9. 2019Segmentation-Based Deep-Learning Approach for Surface-Defect Detectioncited by 145
  10. 2023AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Modelcited by 111
  11. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  12. 2014On the relation between optimal transport and Schr"odinger bridges: A stochastic control viewpointcited by 99
21 more
  1. 2024PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detectioncited by 98
  2. 2023Unpaired Image-to-Image Translation via Neural Schr"odinger Bridgecited by 96
  3. 2019Graph Embedded Pose Clustering for Anomaly Detectioncited by 95
  4. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  5. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  6. 2024Light and Optimal Schr"odinger Bridge Matchingcited by 45
  7. 2018Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Imagescited by 35
  8. 2024Unsupervised Continual Anomaly Detection with Contrastively-learned Promptcited by 31
  9. 2024Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detectioncited by 28
  10. 2022Towards Open Set Video Anomaly Detectioncited by 26
  11. 2023Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detectioncited by 25
  12. 2024Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detectioncited by 19
  13. 2024Supervised Anomaly Detection for Complex Industrial Imagescited by 18
  14. 2023Tree-Based Diffusion Schr"odinger Bridge with Applications to Wasserstein Barycenterscited by 16
  15. 2023Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detectioncited by 14
  16. 2024COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detectioncited by 13
  17. 2023Edit Temporal-Consistent Videos with Image Diffusion Modelcited by 12
  18. 2024Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detectioncited by 10
  19. 2024MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing Dataset and Benchmark for Text-to-Image Generationcited by 8
  20. 2024Re-Attentional Controllable Video Diffusion Editingcited by 5
  21. 2024Fast Ensembling with Diffusion Schr"odinger Bridgecited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 9.

10 papers.

  1. 2026Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspectivecited by 4
  2. 2026A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detectioncited by 3
  3. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  4. 2026Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detectioncited by 0
  5. 2026IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detectioncited by 0
  6. 2026Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detectioncited by 0
  7. 2026Anomaly-Preference Image Generationcited by 0
  8. 2026Beyond Normal References: Discriminative Few-Shot Anomaly Detectioncited by 0
  9. 2026ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detectioncited by 0
  10. 2026Beyond Static Anchors: Bounded Prototype Conditioning for Language-Free Medical Anomaly Detectioncited by 0