Paper Citation Record · LEDGER

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection

As of 16 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2608.07770.

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

pith.paper-citation-record.v1
2608.07770 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:21:38.485958Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact6
verified fuzzy7
unresolved13
parse uncertain0
malformed identifier6
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d2076e6d-864c-47fe-bd19-fc925d8b07e0 · outbound

This paper cites Oceans 11 Data Repository.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Oceans 11 Data Repository

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:39.594814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eb5887f1-11d0-41b7-9d88-e4b37ba0f0ba · outbound

This paper cites Annomate with microsentryai: Human-in-the-loop aiml-powered inspection tools.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Annomate with microsentryai: Human-in-the-loop aiml-powered inspection tools

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:39.581982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ba8ebd6d-3f23-4938-b6dd-fa95c9d5b4b9 · outbound

This paper cites Anomaly detection in laser powder bed fusion using machine learning: A review,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Anomaly detection in laser powder bed fusion using machine learning: A review,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:38.351978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5a622b1-c792-4853-872c-99858f429428 · outbound

This paper cites Automatic metal parts inspection: Use of thermographic images and anomaly detection algorithms,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Automatic metal parts inspection: Use of thermographic images and anomaly detection algorithms,

Reference 4

Resolution: verified exact; doi, observed 2026-08-11T00:21:38.614006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e8ec180c-9af6-4f3c-948a-5a45d38aa01d · outbound

This paper cites A deep-learning-based in-situ surface anomaly detection methodology for laser directed energy deposition via powder feeding,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection A deep-learning-based in-situ surface anomaly detection methodology for laser directed energy deposition via powder feeding,

Reference 5

Resolution: verified exact; doi, observed 2026-08-11T00:21:38.601758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f332ea5c-9890-4cfb-86a7-1338b75b463b · outbound

This paper cites The via annotation software for images, audio and video,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection The via annotation software for images, audio and video,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:38.364410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 684fc48f-ae7c-484c-add3-784079f9ff73 · outbound

This paper cites Labelme: A database and web-based tool for image annotation,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Labelme: A database and web-based tool for image annotation,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:38.368731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ba3b262-c81b-4f9a-9cb5-25d722680939 · outbound

This paper cites Anomaly detection: A survey,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Anomaly detection: A survey,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:38.372912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d681ef9-0599-485c-bf26-1d03f3f43540 · outbound

This paper cites Deep Learning for Anomaly Detection: A Review doi: 10.1145/3439950,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Deep Learning for Anomaly Detection: A Review doi: 10.1145/3439950,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:38.376839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d3c66eb-7df6-4e5e-aa85-20636c9503f9 · outbound

This paper cites Anomalib: A Deep Learning Library for Anomaly Detection doi: 10.1109/ICIP46576.2022.9897283,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Anomalib: A Deep Learning Library for Anomaly Detection doi: 10.1109/ICIP46576.2022.9897283,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:38.381861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d2c9c90-6750-4e51-8abf-fd075f3f5212 · outbound

This paper cites What makes a good data augmentation for few-shot unsupervised image anomaly detection?.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection What makes a good data augmentation for few-shot unsupervised image anomaly detection?

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-11T00:21:39.164235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9429da1f-307f-4614-8810-ba8d03b8a22c · outbound

This paper cites The MVTec anomaly detection dataset: A comprehensive real-world dataset for unsupervised anomaly detection,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection The MVTec anomaly detection dataset: A comprehensive real-world dataset for unsupervised anomaly detection,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:38.392009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dce2351-e3a9-4314-9bf0-61bd5bec4776 · outbound

This paper cites Deep industrial image anomaly detection: A survey,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Deep industrial image anomaly detection: A survey,

Reference 13

Resolution: malformed identifier; doi_truncated, observed 2026-08-11T00:21:38.567411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 95bfa6c1-9df4-4a7e-87a3-a453c6bec353 · outbound

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

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-11T00:21:39.146987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5f921e49-73d6-4dcc-9b2b-db00e981e006 · outbound

This paper cites EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies,

Reference 15

Resolution: malformed identifier; no resolver link, observed 2026-08-11T00:21:38.406706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 722057b0-7ee8-425c-8ff6-62d04c985f23 · outbound

This paper cites Student-teacher feature pyramid matching for anomaly detection,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Student-teacher feature pyramid matching for anomaly detection,

Reference 17

Resolution: verified exact; doi, observed 2026-08-11T00:21:38.555278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b99d8bbb-af4d-4079-a7f3-6b27e6947933 · outbound

This paper cites Towards Total Recall in Industrial Anomaly Detection,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Towards Total Recall in Industrial Anomaly Detection,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:38.420983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebe099f2-9054-4ee9-8562-1bd3de994452 · outbound

This paper cites PaDiM: A patch dis- tribution modeling framework for anomaly detection and localization,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection PaDiM: A patch dis- tribution modeling framework for anomaly detection and localization,

Reference 19

Resolution: malformed identifier; doi_truncated, observed 2026-08-11T00:21:38.541539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 32ec5883-0a7a-45b7-a743-8d3f70d5942d · outbound

This paper cites CFA: Coupled-hypersphere-based fea- ture adaptation for target-oriented anomaly localization,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection CFA: Coupled-hypersphere-based fea- ture adaptation for target-oriented anomaly localization,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:38.429113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bd3ac05-fa37-490e-ace2-47ed8c511f4c · outbound

This paper cites AnomalyDINO: Boosting Patch-based Few-Shot Anomaly Detection with DINOv2,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection AnomalyDINO: Boosting Patch-based Few-Shot Anomaly Detection with DINOv2,

Reference 21

Resolution: malformed identifier; no resolver link, observed 2026-08-11T00:21:38.433567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96d4b774-2c04-4d3f-9e98-1b9289d919c2 · outbound

This paper cites SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-11T00:21:38.826220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e3294870-ba0c-4a78-ad19-ba8851e9ab11 · outbound

This paper cites FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:38.442041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 237cd5db-f1fc-48c5-a24f-f307a1e79e5d · outbound

This paper cites Fully Convolu- tional Cross-Scale-Flows for Image-based Defect Detection,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Fully Convolu- tional Cross-Scale-Flows for Image-based Defect Detection,

Reference 25

Resolution: malformed identifier; no resolver link, observed 2026-08-11T00:21:38.449566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61cb56cb-5a1b-4f52-a7b8-9f4ce4b413e6 · outbound

This paper cites U-Flow: A u-shaped normalizing flow for anomaly detection with unsupervised threshold,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection U-Flow: A u-shaped normalizing flow for anomaly detection with unsupervised threshold,

Reference 26

Resolution: malformed identifier; no resolver link, observed 2026-08-11T00:21:38.453160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71b1f222-e5e1-4cdc-bd56-0ae4d225c9e5 · outbound

This paper cites Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:39.569741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4aa0824f-7429-4193-9fe6-865abd51f548 · outbound

This paper cites DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:38.460646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8f34c0d-a924-40c1-8431-19e7ef61899d · outbound

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

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection DSR – a dual subspace re- projection network for surface anomaly detection,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:38.465333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ea92b06-060c-40ba-94e5-b8b47d44274c · outbound

This paper cites GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training [arXiv],.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training [arXiv],

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:39.557524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5c3c13d9-8e86-4dd8-a28e-b88c355b6438 · outbound

This paper cites FRE: A Fast Method For Anomaly Detection And Segmentation [arXiv],.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection FRE: A Fast Method For Anomaly Detection And Segmentation [arXiv],

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:39.544696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 91e1ef83-66d3-40be-af3d-d850feda3042 · outbound

This paper cites Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T00:21:38.476849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e447f304-8dba-41e0-8e1d-0105f9dc0287 · outbound

This paper cites Repository for reproducibility for ICMLA 2026.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection Repository for reproducibility for ICMLA 2026

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:39.532088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8ee87fa6-6de1-4dc5-8af1-4720a7cddee7 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos,.

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection SAM 2: Segment Anything in Images and Videos,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:21:39.512888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.