Paper Citation Record · LEDGER

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection

As of 13 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2412.08189.

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

pith.paper-citation-record.v1
2412.08189 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:12:16.148765Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

44 of 44 outbound references displayed

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verified fuzzy39
unresolved4
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation caf933e8-18e6-4984-9bdf-e667e500bc9d · outbound

This paper cites Efficien- tad: Accurate visual anomaly detection at millisecond-level latencies.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Efficien- tad: Accurate visual anomaly detection at millisecond-level latencies

Reference 1

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Source-reported events for the cited work

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Observation fe7a076b-dd08-4466-83be-d532b76ca14d · outbound

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

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Mvtec ad — a comprehensive real-world dataset for unsupervised anomaly detection

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.937519Z

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Observation 3e1791bd-f458-4ee5-9d57-fc62dc0f596b · outbound

This paper cites Improving unsupervised defect seg- mentation by applying structural similarity to autoencoders.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Improving unsupervised defect seg- mentation by applying structural similarity to autoencoders

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.922136Z

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Observation db585119-f81a-4d9c-981a-a93dea36f664 · outbound

This paper cites Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.906488Z

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Observation bffdea75-d3a1-4772-986f-7939c5ccd06d · outbound

This paper cites The mvtec anomaly detection dataset: A comprehensive real-world dataset for unsuper- vised anomaly detection.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection The mvtec anomaly detection dataset: A comprehensive real-world dataset for unsuper- vised anomaly detection

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.891158Z

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Observation 5b64088a-0d18-4dc3-8a39-7863fd9e6f67 · outbound

This paper cites Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.876448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0a8030c6-1ccc-4a63-885a-1fd6c80d2453 · outbound

This paper cites Mahoney, and Kurt Keutzer.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Mahoney, and Kurt Keutzer

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.862030Z

Source-reported events for the cited work

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Observation 02f53e36-f8e7-45ec-a798-8d9e11f291bc · outbound

This paper cites Easynet: An easy net- work for 3d industrial anomaly detection.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Easynet: An easy net- work for 3d industrial anomaly detection

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.846532Z

Source-reported events for the cited work

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Observation 071413bf-25a1-47e4-a421-136005ad5d1d · outbound

This paper cites Cnn-based autoencoder and post-training quantization for on-device anomaly detection of cartesian coordinate robots.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Cnn-based autoencoder and post-training quantization for on-device anomaly detection of cartesian coordinate robots

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.829618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 790a8ad6-8a5a-45bd-8752-b1567ac9beeb · outbound

This paper cites Sub-image anomaly detection with deep pyramid correspondences.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Sub-image anomaly detection with deep pyramid correspondences

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.813747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d427c902-46e5-4edd-997d-3840a7ee9401 · outbound

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

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Anomaly detection via reverse distillation from one-class embedding

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.797298Z

Source-reported events for the cited work

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Observation 17ab75f3-e921-4c5a-aff4-9dc004db81f9 · outbound

This paper cites Hawq-v2: Hessian aware trace-weighted quantization of neural networks.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Hawq-v2: Hessian aware trace-weighted quantization of neural networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.780512Z

Source-reported events for the cited work

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Observation ce3a8bdb-7bf2-4a74-96db-43b249beb0d0 · outbound

This paper cites Few- shot defect image generation via defect-aware feature manip- ulation.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Few- shot defect image generation via defect-aware feature manip- ulation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.765307Z

Source-reported events for the cited work

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Observation 86b8ce24-5af9-40ee-8db0-e660e2096899 · outbound

This paper cites Learned Step Size Quantization.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Learned Step Size Quantization

Reference 14

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Source-reported events for the cited work

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Observation 3369afda-7898-4c00-859f-39104d3eb6a4 · outbound

This paper cites Differ- entiable soft quantization: Bridging full-precision and low- bit neural networks.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Differ- entiable soft quantization: Bridging full-precision and low- bit neural networks

Reference 15

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Source-reported events for the cited work

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Observation b15a0de9-dc9f-47ac-8912-642338f57ff1 · outbound

This paper cites Gruber, and Paul Tabatabai.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Gruber, and Paul Tabatabai

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.740492Z

Source-reported events for the cited work

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Observation 8d82ece9-3269-4bed-b5e4-d377ad2f809e · outbound

This paper cites Densely connected convolutional net- works.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Densely connected convolutional net- works

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.727154Z

Source-reported events for the cited work

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Observation 8d538176-73a1-47fb-897f-9e25e299c604 · outbound

This paper cites Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization

Reference 18

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Observation b4f79ee5-0724-4816-9f54-4925f54a7408 · outbound

This paper cites A survey of deep learning- based network anomaly detection.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection A survey of deep learning- based network anomaly detection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.712249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 70b36d3e-67a8-4ce9-8be4-75c46faae623 · outbound

This paper cites Brecq: Pushing the limit of post-training quantization by block reconstruction.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Brecq: Pushing the limit of post-training quantization by block reconstruction

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.697022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d60c6a28-59f0-49d1-b2e8-af471d362870 · outbound

This paper cites Radke, and Octavia Camps.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Radke, and Octavia Camps

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.682218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a4ebf2ce-1ab2-45c1-a03d-245946d6f96e · outbound

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

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Simplenet: A simple network for image anomaly detection and localization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.663949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 20753f50-2e28-4e75-93eb-6db0933edc2b · outbound

This paper cites Remov- ing anomalies as noises for industrial defect localization.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Remov- ing anomalies as noises for industrial defect localization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.647007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5b22699e-13a3-4c18-9f0d-af220bdb004f · outbound

This paper cites Ompq: Orthogonal mixed precision quantization.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Ompq: Orthogonal mixed precision quantization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.632393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4852a3a5-ae8a-4c80-b369-b92a1737a878 · outbound

This paper cites Data-free quantization through weight equal- ization and bias correction.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Data-free quantization through weight equal- ization and bias correction

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.617029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 38722ff6-5931-4d82-80d1-7721909fb04d · outbound

This paper cites Variational inference with normalizing flows.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Variational inference with normalizing flows

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.602985Z

Source-reported events for the cited work

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Observation 407fc091-5722-4d92-b245-583685c051b2 · outbound

This paper cites Moes- lund, and Mubarak Shah.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Moes- lund, and Mubarak Shah

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.585297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f7f0bf59-e668-4ef7-8344-b92d35522712 · outbound

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

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Towards to- tal recall in industrial anomaly detection

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.491118Z

Source-reported events for the cited work

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Observation f9450fa5-c3c2-4655-87e3-55061f4db6ee · outbound

This paper cites Same same but differnet: Semi-supervised defect detection with normalizing flows.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Same same but differnet: Semi-supervised defect detection with normalizing flows

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.476550Z

Source-reported events for the cited work

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Observation f21fd3d7-725a-4f7b-97fd-0318b453c6f1 · outbound

This paper cites Kauffmann, Robert A.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Kauffmann, Robert A

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.460539Z

Source-reported events for the cited work

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Observation 76536c9e-daf2-4b3b-94d7-860256312775 · outbound

This paper cites Quantized autoen- coder (qae) intrusion detection system for anomaly detection in resource-constrained iot devices using rt-iot2022 dataset.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Quantized autoen- coder (qae) intrusion detection system for anomaly detection in resource-constrained iot devices using rt-iot2022 dataset

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.443477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5078407e-7a63-4416-8e61-3d79a62e7579 · outbound

This paper cites Learning and evaluating representations for deep one-class classification.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Learning and evaluating representations for deep one-class classification

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.426420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 82c9c29c-2ef3-4ee7-afbb-a48dca730a4b · outbound

This paper cites Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval

Reference 34

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Source-reported events for the cited work

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Observation 57c273fa-bb61-48c3-9526-54947d9f2cd4 · outbound

This paper cites Deep learning for unsupervised anomaly lo- calization in industrial images: A survey.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Deep learning for unsupervised anomaly lo- calization in industrial images: A survey

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.410317Z

Source-reported events for the cited work

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Observation 95ae6199-3533-420d-9b35-a6f68aa07b1a · outbound

This paper cites Im-iad: Indus- trial image anomaly detection benchmark in manufacturing.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Im-iad: Indus- trial image anomaly detection benchmark in manufacturing

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.392315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bfad1840-99da-4d71-8a18-2b9d90259ccf · outbound

This paper cites Learning semantic context from nor- mal samples for unsupervised anomaly detection.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Learning semantic context from nor- mal samples for unsupervised anomaly detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.375562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bfa74b0d-5efc-49a3-90b2-87c7f59e39e1 · outbound

This paper cites Focus the discrepancy: Intra-and inter- correlation learning for image anomaly detection.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Focus the discrepancy: Intra-and inter- correlation learning for image anomaly detection

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.359341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e58df5f7-794e-42cf-8607-333fac5561ea · outbound

This paper cites A unified model for multi-class anomaly detection.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection A unified model for multi-class anomaly detection

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.343381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f763055a-93fb-484a-b952-3ddb1d10ae69 · outbound

This paper cites Wide residual net- works.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Wide residual net- works

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.327288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3c3f9835-5295-4481-bf8d-b543c3bd2798 · outbound

This paper cites Dr- AEm – a discriminatively trained reconstruction embedding for surface anomaly detection.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Dr- AEm – a discriminatively trained reconstruction embedding for surface anomaly detection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.309836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e05d089e-8821-438c-84ee-f87669efb365 · outbound

This paper cites Recon- struction by inpainting for visual anomaly detection.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Recon- struction by inpainting for visual anomaly detection

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.293583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0b6b5205-ffe0-40b1-a9b9-a6a6512f9e4a · outbound

This paper cites Con- textual affinity distillation for image anomaly detection.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Con- textual affinity distillation for image anomaly detection

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.277534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 10a0600f-31ff-4c47-8cd6-e2cd6b7294c8 · outbound

This paper cites Unsupervised surface anomaly detection with diffusion probabilistic model.

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Unsupervised surface anomaly detection with diffusion probabilistic model

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.260198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d15e265e-68fe-47a3-8fe6-08c6c1f1e40e · outbound

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

Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection Spot-the-difference self-supervised pre- training for anomaly detection and segmentation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:12:16.243961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.