Paper Citation Record · LEDGER

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection

As of 9 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2505.17551.

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

pith.paper-citation-record.v1
2505.17551 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:51:29.971792Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:03:10.663948Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T11:03:11.743055Z

Reference resolution

41 of 41 outbound references displayed

verified exact0
verified fuzzy30
unresolved11
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b16fe89d-6dd7-4de2-a8d9-bb78e175c78c · outbound

This paper cites An anomaly feature-editing- based adversarial network for texture defect visual inspection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection An anomaly feature-editing- based adversarial network for texture defect visual inspection,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:37.047096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 642ae352-6d03-409f-8581-3febee19186b · outbound

This paper cites Collaborative discrepancy opti- mization for reliable image anomaly localization,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Collaborative discrepancy opti- mization for reliable image anomaly localization,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:36.860972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a2373509-4489-4516-87b4-225b9a6bfae2 · outbound

This paper cites Prior normality prompt transformer for multiclass industrial image anomaly detection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Prior normality prompt transformer for multiclass industrial image anomaly detection,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:36.676350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 09153b98-c15a-4737-8bd2-9f6e27f3ce34 · outbound

This paper cites Memorizing normality to detect anomaly: Memory- augmented deep autoencoder for unsupervised anomaly detection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Memorizing normality to detect anomaly: Memory- augmented deep autoencoder for unsupervised anomaly detection,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:25.862567Z

Source-reported events for the cited work

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Observation b1b8d1c7-949a-41fa-94ce-87288775d48f · outbound

This paper cites Reconstruction by inpainting for visual anomaly detection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Reconstruction by inpainting for visual anomaly detection,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:26.006802Z

Source-reported events for the cited work

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Observation be7cbd29-6683-4e50-882c-f162621bbd4e · outbound

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

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Anomaly detection via reverse distillation from one-class embedding,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:26.199937Z

Source-reported events for the cited work

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Observation 1725e4ed-be1b-4461-9b64-87dea367363c · outbound

This paper cites Varad: Lightweight high- resolution image anomaly detection via visual autoregressive modeling,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Varad: Lightweight high- resolution image anomaly detection via visual autoregressive modeling,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:36.472207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 809ec635-7bc2-46b4-9ea9-8d72e0ee14f0 · outbound

This paper cites Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:26.462832Z

Source-reported events for the cited work

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Observation bf3cec81-088d-4d27-a2e3-152ae3dc8021 · outbound

This paper cites Multiresolution knowledge distillation for anomaly detection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Multiresolution knowledge distillation for anomaly detection,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:36.282828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 569f0ed4-9506-4a31-a126-ea5df06891ca · outbound

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

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Towards total recall in industrial anomaly detection,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:26.755920Z

Source-reported events for the cited work

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Observation 7aebb4d7-04cf-4486-af93-409cb287d2c4 · outbound

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

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Cfa: Coupled-hypersphere-based fea- ture adaptation for target-oriented anomaly localization,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:26.826624Z

Source-reported events for the cited work

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Observation 689ac877-7a20-46c7-92b8-a15dc85a0814 · outbound

This paper cites Pyramidflow: High-resolution defect contrastive localization using pyramid normalizing flow,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Pyramidflow: High-resolution defect contrastive localization using pyramid normalizing flow,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:36.059049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2dd7f431-6a7e-460a-a94d-cf644769ce0d · outbound

This paper cites Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,

Reference 13

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Observation 02844c90-4bbf-439d-ba5b-84e2994cc0e6 · outbound

This paper cites Masked swin transformer unet for industrial anomaly detection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Masked swin transformer unet for industrial anomaly detection,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:35.885198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c5c17a96-49e8-444c-9ac0-7759ae7ce622 · outbound

This paper cites Memseg: A semi-supervised method for image surface defect detection using differences and commonalities,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Memseg: A semi-supervised method for image surface defect detection using differences and commonalities,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:35.705181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d835ce2b-b0c6-41cf-bf4f-5420c55c6607 · outbound

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

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Simplenet: A simple network for image anomaly detection and localization,

Reference 16

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Source-reported events for the cited work

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Observation 98348cce-9ecf-4eb2-88fe-ff73fb06f951 · outbound

This paper cites A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:35.526207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 787a43ce-f49e-4db1-a92e-63056ca76c16 · outbound

This paper cites Supersimplenet: Unifying unsu- pervised and supervised learning for fast and reliable surface defect detection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Supersimplenet: Unifying unsu- pervised and supervised learning for fast and reliable surface defect detection,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:35.358334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a2d4ddbc-1bc2-4fd6-ae83-dfaae2435b9d · outbound

This paper cites Scalable industrial visual anomaly detection with partial semantics aggregation vision transformer,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Scalable industrial visual anomaly detection with partial semantics aggregation vision transformer,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:35.181994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ffc6e1dc-70f2-46d9-b0b1-a3a3e4608c8c · outbound

This paper cites Masked au- toencoders are scalable vision learners,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Masked au- toencoders are scalable vision learners,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:35.066096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 80485954-319d-4644-98d0-38c21bba466f · outbound

This paper cites Hierarchi- cal vector quantized transformer for multi-class unsupervised anomaly detection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Hierarchi- cal vector quantized transformer for multi-class unsupervised anomaly detection,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:34.741489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 567dec37-9812-4f3d-98d9-40eb8bf30bfa · outbound

This paper cites A diffusion-based framework for multi-class anomaly detection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection A diffusion-based framework for multi-class anomaly detection,

Reference 22

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Source-reported events for the cited work

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Observation cf4254fd-d921-4242-92a2-ab0a49a99a58 · outbound

This paper cites Personalizing vision-language models with hybrid prompts for zero- shot anomaly detection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Personalizing vision-language models with hybrid prompts for zero- shot anomaly detection,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:34.443249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 022bbb7c-e027-440c-b957-3ca1c25b6ad3 · outbound

This paper cites Unsupervised anomaly detection and localiza- tion with one model for all category,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Unsupervised anomaly detection and localiza- tion with one model for all category,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:34.159937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c5d0c91f-61b0-441a-aef6-6ad3b1ee7140 · outbound

This paper cites Hierarchical gaussian mixture normalizing flow modeling for unified anomaly detection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Hierarchical gaussian mixture normalizing flow modeling for unified anomaly detection,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:33.864998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 86bf0e8b-3e9d-4a7b-b6b9-9a083433e21e · outbound

This paper cites Multi-confidence guided source-free domain adaption method for point cloud primitive seg- mentation,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Multi-confidence guided source-free domain adaption method for point cloud primitive seg- mentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:33.578743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 60505f59-989d-4c8a-baa9-5a9f5cc37c9a · outbound

This paper cites An incremental unified framework for small defect inspection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection An incremental unified framework for small defect inspection,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:33.355930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4cca61a7-f692-4f64-8bb0-bccca9bfb95c · outbound

This paper cites Deep one-class classifi- cation via interpolated gaussian descriptor,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Deep one-class classifi- cation via interpolated gaussian descriptor,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:33.062521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 22004d39-ca2d-4e0f-b1dd-9308384904e5 · outbound

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

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Dsr-a dual subspace re- projection network for surface anomaly detection,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:32.792290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2a97a090-2514-4479-8391-701a0116588b · outbound

This paper cites Progressive bound- ary guided anomaly synthesis for industrial anomaly detection,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Progressive bound- ary guided anomaly synthesis for industrial anomaly detection,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:32.539438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3ab5eab2-04e5-4164-8a57-4da00d62b4e8 · outbound

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

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection A unified model for multi-class anomaly detection,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:32.283877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0a2c284d-ddda-4ad5-9d99-de0cfcf721f3 · outbound

This paper cites Learning to detect multi-class anomalies with just one normal image prompt,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Learning to detect multi-class anomalies with just one normal image prompt,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:32.054406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b5a1aace-8990-408c-b8ca-cdfaa85db9de · outbound

This paper cites Probabilistic boundary-guided point cloud primitive segmentation network,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Probabilistic boundary-guided point cloud primitive segmentation network,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:31.787108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation efc3dde0-3475-4f7a-9633-bf8f32ac28a6 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Imagenet: A large-scale hierarchical image database,

Reference 34

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Source-reported events for the cited work

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Observation e22336c3-358c-44c8-9c52-2a76ecb20c33 · outbound

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

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Mvtec ad-a comprehensive real-world dataset for unsupervised anomaly detection,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:31.483381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 439f56a6-7806-402d-a7d4-e41028be8b29 · outbound

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

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Spot-the- difference self-supervised pre-training for anomaly detection and seg- mentation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:31.165422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 88fc0b4a-c120-46bf-bdfb-2b7ddee509da · outbound

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

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Deep learning-based defect detection of metal parts: evaluating current meth- ods in complex conditions,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:30.873353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a4c03809-034c-4acc-b7c8-138713e2d927 · outbound

This paper cites A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection

Reference 38

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Source-reported events for the cited work

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Observation cb99c97d-d13a-479b-b245-71081fffebdd · outbound

This paper cites Wide residual networks,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Wide residual networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:30.663724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c785fefb-9e1a-47be-b18e-34eb6db10ef6 · outbound

This paper cites Deep residual learning for image recognition,.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection Deep residual learning for image recognition,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:30.393015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f1c77195-1b88-421d-a17c-6247b0f11058 · outbound

This paper cites degree with the Department of Precision Instrument, Tsinghua University, Beijing, China.

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection degree with the Department of Precision Instrument, Tsinghua University, Beijing, China

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:51:30.182568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 08216d61-689e-4562-8a44-c30aa7777c88 · inbound

INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning cites this paper.

INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:03:11.766368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.