Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T18:12:16.148765Z
Paper Citation Record · LEDGER
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection
As of 15 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
44 of 44 outbound references displayed
- verified exact1
- verified fuzzy39
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch0
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T18:12:15.927815Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
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 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
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 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
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 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
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 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 2026-08-15T06:32:42.880941+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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T18:12:15.996458Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T18:12:16.001811Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T18:12:16.209734Z
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 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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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 2026-08-15T06:32:42.880941+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
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- verified fuzzy
- raw_fallback, observed 2026-08-11T18:12:16.491118Z
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 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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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
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- verified fuzzy
- raw_fallback, observed 2026-08-11T18:12:16.460539Z
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 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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T18:12:16.095267Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+00:00.
Pith citing papers
No inbound Pith citation observations are available.