Paper Citation Record · LEDGER

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data

As of 23 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2411.16110.

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

pith.paper-citation-record.v1
2411.16110 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:37:40.135365Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:46:17.208839Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

59 of 59 outbound references displayed

verified exact0
verified fuzzy52
unresolved6
parse uncertain0
malformed identifier1
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External citation measurements

0
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

Observation 4060ff71-9ff2-4d8c-a841-98bb84213353 · outbound

This paper cites A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06a1b833-87e2-4d55-a44d-936d8f029f8f · outbound

This paper cites PNI: indus- trial anomaly detection using position and neighborhood in- formation.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data PNI: indus- trial anomaly detection using position and neighborhood in- formation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:41.151526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 148c0263-32c1-4c75-a53b-c91745e468c0 · outbound

This paper cites MVTec AD — A Comprehensive Real- World Dataset for Unsupervised Anomaly Detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data MVTec AD — A Comprehensive Real- World Dataset for Unsupervised Anomaly Detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:41.135872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d69d960e-655d-48e6-9510-c506ec9d8020 · outbound

This paper cites Lof: identifying density-based local outliers.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Lof: identifying density-based local outliers

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:41.119204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2f83c314-3b46-4e07-8225-676874a60747 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Emerg- ing properties in self-supervised vision transformers

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:41.104352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 20b0e1df-6bfa-4ebc-b462-ec729dd2daba · outbound

This paper cites Deep Learning for Anomaly Detection: A Survey.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Deep Learning for Anomaly Detection: A Survey

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:39.876176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d30ce2d9-402b-40c4-ba2d-4ebf5925e409 · outbound

This paper cites Deep one-class classification via interpolated gaussian descriptor.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Deep one-class classification via interpolated gaussian descriptor

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:41.089156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 26205791-3f92-43d6-aa86-73f81c7451e7 · outbound

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

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:39.887501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0844fe81-3628-4a61-a87c-5f10dae608d4 · outbound

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

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data PaDim: a patch distribution modeling framework for anomaly detection and localization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:41.073757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 56e18d92-a653-4287-9f47-caf2ba1886c6 · outbound

This paper cites Anomaly detection via reverse distillation from one-class embedding.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Anomaly detection via reverse distillation from one-class embedding

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:41.058686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b6e5338d-2351-421b-95e9-aa530202d5af · outbound

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

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Catch- ing both gray and black swans: Open-set supervised anomaly detection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:41.043062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5fc3bc49-0a0d-4dc4-9f3d-7ff848bc4929 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:41.027823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b684ae15-f106-4175-9d47-be8cd22b7cb9 · outbound

This paper cites Robust anomaly detec- tion and backdoor attack detection via differential privacy.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Robust anomaly detec- tion and backdoor attack detection via differential privacy

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:41.011463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 81db353d-e959-417d-b1f3-3416abf2dbea · outbound

This paper cites MIST: Multiple instance self-training framework for video anomaly detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data MIST: Multiple instance self-training framework for video anomaly detection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.995276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f9517f3e-d9fd-4cfd-80ae-ed7538960668 · outbound

This paper cites Ro- bust Loss Functions under Label Noise for Deep Neural Net- works.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Ro- bust Loss Functions under Label Noise for Deep Neural Net- works

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.979529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 02f0b6a1-78e1-4bc7-9c51-1b5ac8b82939 · outbound

This paper cites Surface defect saliency of magnetic tile.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Surface defect saliency of magnetic tile

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.963754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2a6d02d0-5f37-45e6-affa-a6f9ed7d7b59 · outbound

This paper cites FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data, 2025.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data, 2025

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.947636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7cf26f80-c724-4335-a766-58768e66300a · outbound

This paper cites Supplemen- tary document for fun-ad: Fully unsupervised learning for anomaly detection with noisy training data, 2025.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Supplemen- tary document for fun-ad: Fully unsupervised learning for anomaly detection with noisy training data, 2025

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.932095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 234ec5f0-fde0-4fd3-bee3-abcce4d445ab · outbound

This paper cites Learning multiple layers of features from tiny images.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Learning multiple layers of features from tiny images

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.915631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e313d6e0-163b-4ff9-a892-75c826adb6f1 · outbound

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

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Cutpaste: Self-supervised learning for anomaly de- tection and localization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.900243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c6f8a68d-53a2-48ad-961c-122ee775efcb · outbound

This paper cites Deep unsupervised anomaly detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Deep unsupervised anomaly detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.884019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4b3823ac-9232-479a-a544-b87df53b1208 · outbound

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

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Simplenet: A simple network for image anomaly detection and localization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.866723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f2c6c6df-7782-483c-aeb0-e6d932d8090f · outbound

This paper cites A compre- hensive survey on graph anomaly detection with deep learn- ing.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data A compre- hensive survey on graph anomaly detection with deep learn- ing

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.850714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c84215f4-152a-4a7a-8c6f-fcaa79644950 · outbound

This paper cites One-Class SVMs for Document Classification.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data One-Class SVMs for Document Classification

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.834043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ffabaa4a-64e5-4127-ada2-5f0d648bec33 · outbound

This paper cites Inter- realization channels: Unsupervised anomaly detection be- yond one-class classification.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Inter- realization channels: Unsupervised anomaly detection be- yond one-class classification

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.818573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c748ba40-23bf-42ed-84e9-e44c06510a35 · outbound

This paper cites Self-trained deep ordinal regression for end-to-end video anomaly detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Self-trained deep ordinal regression for end-to-end video anomaly detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.802627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2b264810-b0e2-4dd4-9582-80b2cfc4c1f2 · outbound

This paper cites Latent outlier exposure for anomaly detec- tion with contaminated data.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Latent outlier exposure for anomaly detec- tion with contaminated data

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.786289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 077604a5-39cd-4472-b9b5-8a0d80de9423 · outbound

This paper cites Towards to- tal recall in industrial anomaly detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Towards to- tal recall in industrial anomaly detection

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.770833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2097efc1-72f7-42aa-84b8-cddd649b3870 · outbound

This paper cites Fully convolutional cross-scale-flows for image- based defect detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Fully convolutional cross-scale-flows for image- based defect detection

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.754811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 30192f3e-5afc-42a1-9003-3e109cb2e43f · outbound

This paper cites Natural synthetic anomalies for self-supervised anomaly detection and localization.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Natural synthetic anomalies for self-supervised anomaly detection and localization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.738250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b2f7775d-0321-4867-a61c-727cac77b666 · outbound

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

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Active Learning for Con- volutional Neural Networks: A Core-Set Approach

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.721766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5bfbfd52-f3fc-4a22-a85c-981a03c0ee2f · outbound

This paper cites Anomaly detection using score-based per- turbation resilience.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Anomaly detection using score-based per- turbation resilience

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.705041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d24e0236-8979-465c-99e3-2f28d24a0280 · outbound

This paper cites Revisiting reverse distillation for anomaly detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Revisiting reverse distillation for anomaly detection

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.688099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8fb6facb-22b3-4d38-99ac-05d3ac84a9d4 · outbound

This paper cites Unsupervised feature learn- ing with c-svddnet.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Unsupervised feature learn- ing with c-svddnet

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.671787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 95e08d66-2848-490e-86b3-18c5e00c8ff8 · outbound

This paper cites Hierarchical semi-supervised con- trastive learning for contamination-resistant anomaly detec- tion.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Hierarchical semi-supervised con- trastive learning for contamination-resistant anomaly detec- tion

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.653847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5e206cdc-dc3a-4753-b2c6-5e89e933752f · outbound

This paper cites Glanc- ing at the patch: Anomaly localization with global and lo- cal feature comparison.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Glanc- ing at the patch: Anomaly localization with global and lo- cal feature comparison

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.635840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 407b78b9-62d0-4b41-b26b-15c6ba1b0918 · outbound

This paper cites Diffusion models for medical anomaly detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Diffusion models for medical anomaly detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.617040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7fc5ac83-8e70-4c6b-8f1c-9b60a4485cb6 · outbound

This paper cites SoftPatch: Un- supervised anomaly detection with noisy data.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data SoftPatch: Un- supervised anomaly detection with noisy data

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.597343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c18f6cc8-a245-4840-9392-63116d26f335 · outbound

This paper cites Squid: Deep feature in-painting for unsupervised anomaly detec- tion.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Squid: Deep feature in-painting for unsupervised anomaly detec- tion

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.563009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1c1058be-48df-4807-bcf5-fe3d5533c15f · outbound

This paper cites IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:40.046160Z

Source-reported events for the cited work

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Observation c4ad1e8c-8025-4ff2-ad8b-8a2bd8162901 · outbound

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

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Explicit boundary guided semi-push-pull contrastive learning for supervised anomaly detection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.545695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 31bb2e97-1524-451f-8a3d-6fb3f6d1addf · outbound

This paper cites Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.528743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 31763a71-f2f6-46d8-8024-2fa7cf301f22 · outbound

This paper cites Deep anomaly discovery from unla- beled videos via normality advantage and self-paced refine- ment.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Deep anomaly discovery from unla- beled videos via normality advantage and self-paced refine- ment

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.510505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5d1307a4-c0f8-4f48-88d6-2b97fef80bbc · outbound

This paper cites Generative cooperative learning for unsupervised video anomaly detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Generative cooperative learning for unsupervised video anomaly detection

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.492756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e1d755d4-4e9a-45f0-9b00-b63fd33d27c4 · outbound

This paper cites Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.474205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 24b61e89-2280-4aec-8a9d-3978cff9fdd8 · outbound

This paper cites Dsr– a dual subspace re-projection network for surface anomaly detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Dsr– a dual subspace re-projection network for surface anomaly detection

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.455650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 94d2d993-2aa2-4073-8d97-a79f546ba34c · outbound

This paper cites Prototypical residual networks for anomaly detection and localization.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Prototypical residual networks for anomaly detection and localization

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.437028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a12eeb19-a6d0-4ffc-8b20-53997c43d44e · outbound

This paper cites Destseg: Segmentation guided denoising student-teacher for anomaly detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Destseg: Segmentation guided denoising student-teacher for anomaly detection

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.418175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0e2f3c1f-36a1-4f47-95ac-eb4b6e514672 · outbound

This paper cites RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:40.089004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e56ac49-c6af-441f-9a75-1356de7506a1 · outbound

This paper cites Learning with local and global consistency.Advances in Neural Information Process- ing Systems, 16, 2003.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Learning with local and global consistency.Advances in Neural Information Process- ing Systems, 16, 2003

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.398195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation af407856-f001-4922-9cec-9544e13fe9b2 · outbound

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

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Spot-the-difference self-supervised pre- training for anomaly detection and segmentation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.379751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5be99491-ce09-4215-b384-8cbd1dfd8d16 · outbound

This paper cites Therefore, we aim to bridge this theoretical gap with empirical analy- sis using real-world data.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Therefore, we aim to bridge this theoretical gap with empirical analy- sis using real-world data

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.361991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8f337b73-855e-49cd-8b7c-4a212c5e666c · outbound

This paper cites au- tomobile.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data au- tomobile

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.344721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 61e42b86-d332-4659-bb68-92ca3448ca62 · outbound

This paper cites E takes an image Ii as input and outputs one class token and P patch tokens.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data E takes an image Ii as input and outputs one class token and P patch tokens

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.326103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5e933c59-10e1-42cc-b56f-b919ae9d1eba · outbound

This paper cites 1 demon- strates the performance of FUN-AD according to the con- tamination ratio in the training dataset.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data 1 demon- strates the performance of FUN-AD according to the con- tamination ratio in the training dataset

Reference 56

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T13:37:40.309015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1ba17e3e-cb58-413b-a2a7-ee2b26bea345 · outbound

This paper cites 2 shows some anomaly localization results yielded by FUN-AD.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data 2 shows some anomaly localization results yielded by FUN-AD

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.292462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 64668209-1031-4a96-9f16-b8e095fc63c0 · outbound

This paper cites 7, 8, 9, 10.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data 7, 8, 9, 10

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.274981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5f40e8c2-2f96-46c0-b785-2f03cfd4f992 · outbound

This paper cites when one type of anomaly dominates.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data when one type of anomaly dominates

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:40.257836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4dc1c526-5ec2-40ca-b0d8-8f7ac5b4dbd6 · outbound

This paper cites an unresolved cited work.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:37:40.579831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 32229d58-ab22-45dd-a400-384be5bfd217 · inbound

Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection cites this paper.

Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:46:22.170831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.