Paper Citation Record · LEDGER

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection

As of 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2506.21398.

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

pith.paper-citation-record.v1
2506.21398 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:33:54.832028Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T13:48:39.954133Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T13:54:44.027524Z

Reference resolution

43 of 43 outbound references displayed

verified exact1
verified fuzzy28
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d67f649c-1db5-4012-897e-fc9bce9b7a57 · outbound

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

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Mvtec ad–a com- prehensive real-world dataset for unsupervised anomaly detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:34:00.888359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d6ca8457-8e46-496c-8710-5a5b5783dba4 · outbound

This paper cites Convex optimization: Algorithms and complexity.Foundations and Trends®in Machine Learning, 8(3-4):231–357, 2015.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Convex optimization: Algorithms and complexity.Foundations and Trends®in Machine Learning, 8(3-4):231–357, 2015

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:34:00.536788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 72648e61-bacd-4d9c-bcbf-aa872511d91b · outbound

This paper cites Sub-Image Anomaly Detection with Deep Pyramid Correspondences.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:48.590112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d9997f1-d538-4933-87b4-e0b0a33619f1 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.Ad- vances in neural information processing systems, 26, 2013.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Sinkhorn distances: Lightspeed computation of optimal transport.Ad- vances in neural information processing systems, 26, 2013

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:34:00.319823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3dd45a67-fed9-41d6-9d74-248a0f616f55 · outbound

This paper cites Anomalydino: Boostingpatch-basedfew-shotanomalydetectionwithdinov2.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Anomalydino: Boostingpatch-basedfew-shotanomalydetectionwithdinov2

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:34:00.037755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c967aa8f-deae-4c8d-8d77-6f57dc0e6303 · outbound

This paper cites Padim: a patch distribution modeling framework for anomaly detection and localization.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:59.716581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c12438d7-9726-41f4-8d4f-9c60a337732b · outbound

This paper cites Fas- trecon: Few-shot industrial anomaly detection via fast feature reconstruction.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Fas- trecon: Few-shot industrial anomaly detection via fast feature reconstruction

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:59.386232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ccc131e4-5347-4b59-afed-94a3b7b2f78f · outbound

This paper cites Memorizingnormalitytodetectanomaly: Memory- augmented deep autoencoder for unsupervised anomaly detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Memorizingnormalitytodetectanomaly: Memory- augmented deep autoencoder for unsupervised anomaly detection

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:59.199331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5d30ebb8-2c37-49a4-8fae-c21affe6caf1 · outbound

This paper cites Anomalygpt: Detecting industrial anomalies using large vision-language models.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Anomalygpt: Detecting industrial anomalies using large vision-language models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:58.983617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f88576ce-6631-4c0b-a354-906d77d79715 · outbound

This paper cites DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:49.554945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cf2cf2a-79c1-4fe0-8fb5-945f09f18f4a · outbound

This paper cites Maskr-cnn.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Maskr-cnn

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:58.794194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 44b579db-3c09-42d8-95fc-e956b7b3780b · outbound

This paper cites Registration based few-shot anomaly detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Registration based few-shot anomaly detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:58.600195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0d819646-5220-4fce-b682-3e517a0bd283 · outbound

This paper cites Winclip: Zero-/few-shot anomaly classification and segmentation.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Winclip: Zero-/few-shot anomaly classification and segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:58.443613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 00b03f05-e662-4ae7-8f2f-282781ecf599 · outbound

This paper cites Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:58.277423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 105dd8e7-dcf5-4548-9346-e672c064c156 · outbound

This paper cites Anomaly detection for predictive maintenance in industry 4.0-a survey.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Anomaly detection for predictive maintenance in industry 4.0-a survey

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:58.066521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 341b7865-db6c-4941-a9aa-217f50a2ebef · outbound

This paper cites Promptad: Learning prompts with only normal samples for few-shot anom- aly detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Promptad: Learning prompts with only normal samples for few-shot anom- aly detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.909291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 53243fd0-4c1a-4401-b024-15de82df4752 · outbound

This paper cites Deep industrial image anomaly detection: A survey.Machine Intelligence Research, 21(1):104–135, 2024.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Deep industrial image anomaly detection: A survey.Machine Intelligence Research, 21(1):104–135, 2024

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:50.490783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e783961c-18a6-457a-8b4f-0690b5b3cb1e · outbound

This paper cites Ttt++: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems, 34:21808–21820, 2021.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Ttt++: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems, 34:21808–21820, 2021

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.706076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2543ccec-f207-4ee6-8aa9-be14c0674f23 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Swin transformer: Hierarchical vision transformer using shifted windows

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:50.706988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 128295d6-821c-474f-bf59-ce10b8bfbd66 · outbound

This paper cites Simplenet: A simple network for image anomaly detection and localization.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Simplenet: A simple network for image anomaly detection and localization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.550928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 93eddd04-90e9-47e0-8485-6c3d68db2e8f · outbound

This paper cites Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:33:55.037510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c78c28cb-ad54-4978-9a50-69179c994165 · outbound

This paper cites Hierarchical vector quantized transformer for multi-class unsupervised anomaly de- tection.Advances in Neural Information Processing Systems, 36, 2024.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Hierarchical vector quantized transformer for multi-class unsupervised anomaly de- tection.Advances in Neural Information Processing Systems, 36, 2024

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.393038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1fa4decd-322e-4c25-93cb-0d1aadbf1c88 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection DINOv2: Learning Robust Visual Features without Supervision

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:51.211770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 237c454a-f321-41f7-a99e-96e99c4b2320 · outbound

This paper cites The matrix cookbook.Technical University of Denmark, 7(15):510, 2008.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection The matrix cookbook.Technical University of Denmark, 7(15):510, 2008

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:51.350016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1124c38-368b-4153-906d-a5f11f03543b · outbound

This paper cites Computational optimal transport: With applications to data science.Foundations and Trends®in Machine Learning, 11(5-6):355–607, 2019.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Computational optimal transport: With applications to data science.Foundations and Trends®in Machine Learning, 11(5-6):355–607, 2019

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:51.470682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ce41bb9-1081-4309-96b4-8802d655cfa6 · outbound

This paper cites Learning transferable visual models from natural language supervision.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Learning transferable visual models from natural language supervision

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:51.626009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d02cc696-c7c0-45b3-9356-37424d811b1b · outbound

This paper cites Towards total recall in industrial anomaly detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Towards total recall in industrial anomaly detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.203917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c4641163-c5fd-4d56-ad5d-1d70c5eda4df · outbound

This paper cites Optimizing PatchCore for Few/many-shot Anomaly Detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Optimizing PatchCore for Few/many-shot Anomaly Detection

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:53.440953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5f2d385-1827-422f-8507-9e97ff67cffc · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:53.528808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19a02799-0cb7-4c8d-a20b-565b6c0c1d98 · outbound

This paper cites Prototypicalnetworksforfew-shotlearning.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Prototypicalnetworksforfew-shotlearning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.011466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cec02c39-fae0-4439-a754-41925e328828 · outbound

This paper cites Test- time training with self-supervision for generalization under distribution shifts.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Test- time training with self-supervision for generalization under distribution shifts

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:56.782708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 30a4150b-6010-4ef1-907d-0b399dc3087f · outbound

This paper cites Learning to compare: Relation network for few-shot learning.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Learning to compare: Relation network for few-shot learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:56.565663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8c85f32d-879f-4912-ba72-b2d2f6879925 · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:53.864432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28f9946a-3863-4f6b-8370-6e091186d457 · outbound

This paper cites Foct: Few-shot industrial anomaly detection with foreground- aware online conditional transport.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Foct: Few-shot industrial anomaly detection with foreground- aware online conditional transport

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:56.398248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 57a2ce7e-18ff-481c-a4de-b3dd556fa1c8 · outbound

This paper cites Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:54.030153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6f2f54c-b99b-43df-8e87-df6679e5ed85 · outbound

This paper cites Generalizing from a few examples: A survey on few-shot learning.ACM computing surveys (csur), 53(3):1–34, 2020.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Generalizing from a few examples: A survey on few-shot learning.ACM computing surveys (csur), 53(3):1–34, 2020

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:54.102061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a7fccbb-e35a-4730-9221-bab4707137d9 · outbound

This paper cites Learning unsuper- vised metaformer for anomaly detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Learning unsuper- vised metaformer for anomaly detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:56.148319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 355ca069-dcaf-4ada-b004-29436688bbc8 · outbound

This paper cites Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:55.945578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 12640d98-1366-4a3c-90f6-1307e2d67106 · outbound

This paper cites Pushing the limits of fewshot anomaly detection in industry vision: Graphcore.ICLR, 2023.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Pushing the limits of fewshot anomaly detection in industry vision: Graphcore.ICLR, 2023

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:55.754778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation be83b5ec-8888-4511-a33a-73bad5fd34b9 · outbound

This paper cites A uni- fied model for multi-class anomaly detection.Advances in Neural Information Processing Systems, 35:4571–4584, 2022.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection A uni- fied model for multi-class anomaly detection.Advances in Neural Information Processing Systems, 35:4571–4584, 2022

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:55.580485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1cbaffa6-a272-4df2-ac90-42f36ee0c78f · outbound

This paper cites Wide Residual Networks.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Wide Residual Networks

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:54.606058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20b1bf85-9f3a-430e-b2ff-bfd3375f982c · outbound

This paper cites Omnial: A unified cnn framework for unsupervised anomaly localization.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Omnial: A unified cnn framework for unsupervised anomaly localization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:55.392795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 718b8e2e-5363-40fb-8124-8fd3851f77f9 · outbound

This paper cites Spot- the-difference self-supervised pre-training for anomaly detection and segmentation.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Spot- the-difference self-supervised pre-training for anomaly detection and segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:55.222538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 9d82e1a9-1097-4df2-9174-656b9f0320b8 · inbound

Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces cites this paper.

Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-06-30T13:54:44.028873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.