Paper Citation Record · LEDGER

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection

As of 17 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2502.20981.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.20981 v3

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

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measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:21:50.333432Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T17:22:04.769411Z

Reference resolution

51 of 51 outbound references displayed

verified exact12
verified fuzzy35
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f55d0277-5500-4904-9d20-0b89a3d35d26 · outbound

This paper cites Ub- normal: New benchmark for supervised open-set video anomaly detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Ub- normal: New benchmark for supervised open-set video anomaly detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.377224Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a1132d49-616c-4c3c-9e1f-a5721f1fde10 · outbound

This paper cites Supervised anomaly detection for complex indus- trial images.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Supervised anomaly detection for complex indus- trial images

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.362676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 72c2c95c-b024-4827-9d76-f6b42b8a7aaa · outbound

This paper cites Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.338092Z

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Observation 45f48325-0a68-4d07-999a-626e271f05d2 · outbound

This paper cites Hyperkvasir, a comprehensive multi-class im- age and video dataset for gastrointestinal endoscopy.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Hyperkvasir, a comprehensive multi-class im- age and video dataset for gastrointestinal endoscopy

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.412847Z

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Observation b2476186-b9b6-4fc1-beba-a86c2b89f930 · outbound

This paper cites On the relation between optimal transport and schr ¨odinger bridges: A stochastic control viewpoint.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection On the relation between optimal transport and schr ¨odinger bridges: A stochastic control viewpoint

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.327909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6ade42e5-97a9-43be-a329-80247597ae6b · outbound

This paper cites Generating and reweighting dense contrastive pat- terns for unsupervised anomaly detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Generating and reweighting dense contrastive pat- terns for unsupervised anomaly detection

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.310614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ad142dd3-6bac-4f37-8219-91ada252581c · outbound

This paper cites Diffusion schr¨odinger bridge with applications to score-based generative modeling.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Diffusion schr¨odinger bridge with applications to score-based generative modeling

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.344557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f22859e6-27ab-4022-93cf-bb177795de63 · outbound

This paper cites Automatic classification of defective photovoltaic module cells in electroluminescence images.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Automatic classification of defective photovoltaic module cells in electroluminescence images

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.359039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 17e127f6-7e22-4fc4-97e9-9a05545b7951 · outbound

This paper cites Catching both gray and black swans: Open-set supervised anomaly detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Catching both gray and black swans: Open-set supervised anomaly detection

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.364181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d57de710-8cb2-465c-bf1b-e4fe52ee071b · outbound

This paper cites Light and optimal schr ¨odinger bridge matching.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Light and optimal schr ¨odinger bridge matching

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.341740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b774cea5-f833-4ddc-9ff3-0357b65e6447 · outbound

This paper cites Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:15:18.082419Z

Source-reported events for the cited work

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Observation 664cccee-e182-481c-add2-71e218b06eb5 · outbound

This paper cites Anomalyd- iffusion: Few-shot anomaly image generation with diffusion model.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Anomalyd- iffusion: Few-shot anomaly image generation with diffusion model

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.316981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ac7652a9-7c98-469a-9077-e233caaf1b86 · outbound

This paper cites Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.373574Z

Source-reported events for the cited work

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Observation e2162834-07ff-448a-b2bc-92fab46e44c2 · outbound

This paper cites Unpaired Image-to-Image Translation via Neural Schr\"odinger Bridge.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Unpaired Image-to-Image Translation via Neural Schr\"odinger Bridge

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:15:18.062201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d2b62eac-a6a7-4dee-8b24-0884d72fca4c · outbound

This paper cites San- flow: Semantic-aware normalizing flow for anomaly detec- tion.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection San- flow: Semantic-aware normalizing flow for anomaly detec- tion

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.351398Z

Source-reported events for the cited work

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Observation 954e75d3-63c5-4957-8492-5d6c65a98db2 · outbound

This paper cites Fast Ensembling with Diffusion Schr\"odinger Bridge.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Fast Ensembling with Diffusion Schr\"odinger Bridge

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:15:18.056124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a696c5e6-b306-442c-8f9b-e9c44bb39b6d · outbound

This paper cites Light Schr\"odinger Bridge.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Light Schr\"odinger Bridge

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:15:18.071480Z

Source-reported events for the cited work

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Observation e83ac1f3-6565-4099-baca-6b5e9b52f241 · outbound

This paper cites A survey of the Schr\"odinger problem and some of its connections with optimal transport.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection A survey of the Schr\"odinger problem and some of its connections with optimal transport

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:15:18.092302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 79c90104-02f6-4fda-8bd8-6fa444419b8f · outbound

This paper cites Cutpaste: Self-supervised learning for anomaly de- tection and localization.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Cutpaste: Self-supervised learning for anomaly de- tection and localization

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.424973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2f5f05dd-793c-42e3-8a5c-b112a259c6d9 · outbound

This paper cites Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:15:18.042695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b9f343a3-e17e-4788-991b-e09ae54f238e · outbound

This paper cites Promptad: Learn- ing prompts with only normal samples for few-shot anomaly detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Promptad: Learn- ing prompts with only normal samples for few-shot anomaly detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.355543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d64b8986-e181-4c8e-a411-f13e1aa7d4bf · outbound

This paper cites Coft-ad: Contrastive fine-tuning for few-shot anomaly detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Coft-ad: Contrastive fine-tuning for few-shot anomaly detection

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.421934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6094fd2e-1176-4ac0-94eb-ba8ff2ef7649 · outbound

This paper cites Deep generalized schr ¨odinger bridge.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Deep generalized schr ¨odinger bridge

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.416062Z

Source-reported events for the cited work

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Observation aa0c55ef-5f0f-47c1-8f5a-e04e3557bb4f · outbound

This paper cites Generalized Schr\"odinger Bridge Matching.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Generalized Schr\"odinger Bridge Matching

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:15:18.061800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation aec12a47-7406-4264-85c3-81a95b528489 · outbound

This paper cites I$^2$SB: Image-to-Image Schr\"odinger Bridge.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection I$^2$SB: Image-to-Image Schr\"odinger Bridge

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:15:18.066844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 26548fab-af2e-4088-b611-73cad8f280cd · outbound

This paper cites Unsupervised continual anomaly detection with contrastively-learned prompt.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Unsupervised continual anomaly detection with contrastively-learned prompt

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.404489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8e6d4867-f2d3-4d5b-a3dd-11babf5657a9 · outbound

This paper cites Margin learning embedded prediction for video anomaly detection with a few anomalies.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Margin learning embedded prediction for video anomaly detection with a few anomalies

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.388137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1ea6633b-d1eb-4ce1-a079-be689891f885 · outbound

This paper cites Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:15:18.077119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e74b41da-764d-4748-9b16-3493959c3d7f · outbound

This paper cites Decoupled Weight Decay Regularization.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Decoupled Weight Decay Regularization

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:15:18.108820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 56963430-e896-4358-8837-d50a874063ee · outbound

This paper cites Directional statistics.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Directional statistics

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.410118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0fda9732-b6d9-4a8a-b7a6-920db6cca5af · outbound

This paper cites Graph embedded pose clustering for anomaly detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Graph embedded pose clustering for anomaly detection

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.383855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f2381c19-f9d8-42cf-a3c4-d243062bfeaf · outbound

This paper cites Tree-based diffusion schr¨odinger bridge with applications to wasserstein barycenters.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Tree-based diffusion schr¨odinger bridge with applications to wasserstein barycenters

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.370257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0a261ad4-17a7-45b0-93b0-6356354bb0df · outbound

This paper cites Deep anomaly detection with deviation networks.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Deep anomaly detection with deviation networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.313744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eaa15935-414c-49e0-bbde-f696826dc6a6 · outbound

This paper cites Explainable Deep Few-shot Anomaly Detection with Deviation Networks.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Explainable Deep Few-shot Anomaly Detection with Deviation Networks

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-05-23T02:15:18.097332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2e4e0f1d-e2bd-490c-b031-540c3d4dc161 · outbound

This paper cites Focal loss for dense ob- ject detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Focal loss for dense ob- ject detection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.324820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1b5ab676-5668-4892-b48f-4b81970f047a · outbound

This paper cites Multiresolution knowledge distillation for anomaly detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Multiresolution knowledge distillation for anomaly detection

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.397067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 31f7f4bb-1709-4f3d-8755-127dc7f09f92 · outbound

This paper cites Diffusion schr ¨odinger bridge matching.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Diffusion schr ¨odinger bridge matching

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.393764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2d8a7827-5aea-4484-8899-7558151d3b80 · outbound

This paper cites A public fabric database for defect detection methods and results.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection A public fabric database for defect detection methods and results

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.347913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 467582b1-6737-4339-a430-24a42703e71a · outbound

This paper cites Csi: Novelty detection via contrastive learning on dis- tributionally shifted instances.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Csi: Novelty detection via contrastive learning on dis- tributionally shifted instances

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.367654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8dfbe94a-e591-4d66-b9ca-a94496a24738 · outbound

This paper cites Weakly supervised learn- ing for industrial optical inspection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Weakly supervised learn- ing for industrial optical inspection

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.333704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation accabaf6-1f1c-4053-8585-a721331e6590 · outbound

This paper cites Explicit boundary guided semi-push- pull contrastive learning for supervised anomaly detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Explicit boundary guided semi-push- pull contrastive learning for supervised anomaly detection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.366440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8d78fd4a-8b73-425d-a73b-2932f0533c6a · outbound

This paper cites Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:15:18.087797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 79dc3d12-b28a-4f18-8cbc-681f7d5ff8b8 · outbound

This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.329138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cce7849f-8bad-4e7f-9809-18c45f5cfd80 · outbound

This paper cites Anomalyclip: Object-agnostic prompt learn- ing for zero-shot anomaly detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Anomalyclip: Object-agnostic prompt learn- ing for zero-shot anomaly detection

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:15:18.102743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation affccaa5-9fc1-4746-be93-194a5d118715 · outbound

This paper cites Anomaly heterogeneity learning for open-set supervised anomaly detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Anomaly heterogeneity learning for open-set supervised anomaly detection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.380658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f7cca7aa-90e6-4d05-a605-7983ac84571f · outbound

This paper cites Towards open set video anomaly detection.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Towards open set video anomaly detection

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.427907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c24b12b5-d50b-4a85-b98d-dfaab0d946fe · outbound

This paper cites an unresolved cited work.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-05-23T02:15:18.407487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4b48b1b0-a85d-426e-b0a1-9e7cb67e0ddf · outbound

This paper cites 5 presents a comprehensive comparison of the pro- posed DPDL method with state-of-the-art (SOTA) ap- proaches under general settings.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection 5 presents a comprehensive comparison of the pro- posed DPDL method with state-of-the-art (SOTA) ap- proaches under general settings

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.419246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b972a624-14ba-443f-bda1-3a127a53e1c0 · outbound

This paper cites an unresolved cited work.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-05-23T02:15:18.400581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e63e51d4-d996-4fd8-9a63-e458a85a2af8 · outbound

This paper cites an unresolved cited work.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-05-23T02:15:18.307222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c9257c1a-688d-41b9-967c-7fd49ab9f9a0 · outbound

This paper cites (13) and (14) We use Eqns.

Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection (13) and (14) We use Eqns

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T02:15:18.391040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 2a94b2b3-72e8-47b2-b97e-0390e25343ba · inbound

A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects cites this paper.

A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection

Reference 171

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:22:04.818222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.