Paper Citation Record · LEDGER

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection

As of 21 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2505.03412.

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

pith.paper-citation-record.v1
2505.03412 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:55:41.606482Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

27 of 27 outbound references displayed

verified exact1
verified fuzzy20
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4bab83c0-9fae-4638-8433-a70af7c6de79 · outbound

This paper cites YOLO-HMC: An improved method for PCB surface defect detection,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection YOLO-HMC: An improved method for PCB surface defect detection,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:42.172121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 524b010f-37f8-4f5f-a28d-1c7cc9ea4a18 · outbound

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

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:42.156101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 33d3639f-39fc-4e13-8bb7-7a3bf6278131 · outbound

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

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Spot-the- difference self-supervised pre-training for anomaly detection and segmentation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:42.141021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a15780e5-381f-4fa2-a8b9-fd62e3022981 · outbound

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

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Deep learning- based defect detection of metal parts: evaluating current methods in complex conditions,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:42.124381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ae9df90d-5958-41c7-8c04-6a6da1080187 · outbound

This paper cites Generative adversarial nets,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Generative adversarial nets,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T23:55:41.495752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4931d8eb-813d-4d25-8542-c3d5a922733d · outbound

This paper cites Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T23:55:41.501306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db17e820-1cfd-450d-be73-27ed2173b3c5 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Image quality assessment: from error visibility to structural similarity,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:42.096322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a177fc06-4d25-4898-8074-14e53665365c · outbound

This paper cites Fastrecon: Few- shot industrial anomaly detection via fast feature reconstruction,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Fastrecon: Few- shot industrial anomaly detection via fast feature reconstruction,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:42.080420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 72093ea8-f82e-4b31-abfa-d6ba27d6e0b7 · outbound

This paper cites Variational Autoencoder for Anomaly Detection: A Comparative Study.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Variational Autoencoder for Anomaly Detection: A Comparative Study

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T23:55:41.516189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6acd34be-c853-4a8a-b073-a2fdc6f1c7d8 · outbound

This paper cites Learning traces by yourself: Blind image forgery localization via anomaly detection with ViT-VAE,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Learning traces by yourself: Blind image forgery localization via anomaly detection with ViT-VAE,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:42.064811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5a972a2a-7b77-4e36-9ca1-221a2aa629a8 · outbound

This paper cites Variational Autoencoder with Gaussian Random Field prior: Application to unsupervised animal detection in aerial images,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Variational Autoencoder with Gaussian Random Field prior: Application to unsupervised animal detection in aerial images,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:42.048740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 359b5ede-1ad9-41c4-bf7f-06926054807d · outbound

This paper cites MIAD: A maintenance inspection dataset for unsupervised anomaly detection,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection MIAD: A maintenance inspection dataset for unsupervised anomaly detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:42.033103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b84885cc-b33e-41a4-ac0d-5f37c34f8876 · outbound

This paper cites f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:42.017050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 938725e5-83d0-468b-bd6d-6e04a6b9c7ff · outbound

This paper cites Multiscale GAN With Region Adaptive Schemes for Online Inspection of Fabric Flexographic Printing Labels,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Multiscale GAN With Region Adaptive Schemes for Online Inspection of Fabric Flexographic Printing Labels,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:42.000744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4c124e17-39ba-4730-90cb-b0b84c4118d7 · outbound

This paper cites Few-shot defect image generation via defect-aware feature manipulation,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Few-shot defect image generation via defect-aware feature manipulation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:41.985175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f5d8ea0f-fcfa-472b-bd0a-123ca569ee2c · outbound

This paper cites Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:55:41.697757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ece290e4-bd45-4d53-b76d-3e7088f31c60 · outbound

This paper cites Semi-Patchcore: A Novel Two-Staged Method for Semi-supervised Anomaly Detection and Localization,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Semi-Patchcore: A Novel Two-Staged Method for Semi-supervised Anomaly Detection and Localization,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:41.856859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 061cd171-0874-412d-9c92-bf5cd1fd7355 · outbound

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

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Padim: a patch distribution modeling framework for anomaly detection and localization,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:41.842442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 00a14fc9-c901-442a-9387-29ac938c6682 · outbound

This paper cites Towards total recall in industrial anomaly detection,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Towards total recall in industrial anomaly detection,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:41.826518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 42002dc0-0609-4299-ba56-aa1b5ccbf574 · outbound

This paper cites ReConPatch: Contrastive patch representation learning for industrial anomaly detection,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection ReConPatch: Contrastive patch representation learning for industrial anomaly detection,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:41.808801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0067920f-8464-43b5-a063-141f3baf4a76 · outbound

This paper cites Language Models are Few-Shot Learners.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Language Models are Few-Shot Learners

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T23:55:41.577694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2736fd0-6888-4c4c-857a-3a2d0ad666dd · outbound

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

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Anomalygpt: Detecting industrial anomalies using large vision-language models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:41.793420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 587a9033-7773-4190-adda-f6a12ad73cdc · outbound

This paper cites Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T23:55:41.587569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df9be421-42c9-4d62-9f33-876718eea62a · outbound

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

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection DINOv2: Learning Robust Visual Features without Supervision

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T23:55:41.592592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 558ce501-f322-453d-9fcf-30bc5e005441 · outbound

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

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Learning transferable visual models from natural language supervision,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:41.776619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1ae87813-4a69-4265-a216-f576e76027d2 · outbound

This paper cites Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:41.761025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d5a9efca-e518-46a6-a657-a2d0500daaea · outbound

This paper cites Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt,.

CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:55:41.746210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.