Paper Citation Record · LEDGER

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection

As of 20 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2502.01201.

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

pith.paper-citation-record.v1
2502.01201 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T16:18:12.751873Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

46 of 46 outbound references displayed

verified exact2
verified fuzzy28
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5b7347bf-d09f-49d0-a78b-880343d0cdf3 · outbound

This paper cites Diffusion-based data augmentation for skin disease classification: Impact across original medical datasets to fully synthetic images.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Diffusion-based data augmentation for skin disease classification: Impact across original medical datasets to fully synthetic images

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.382443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 93186eec-f69e-4043-8b07-56b0d1c69337 · outbound

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

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.370078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 23861983-ae97-46b4-b91b-169cacd33cb3 · outbound

This paper cites Anomaly detection under distribution shift.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Anomaly detection under distribution shift

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.582667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cbaa7db-8ca4-4a53-8686-eb580afc31c9 · outbound

This paper cites Subject-driven text-to-image generation via apprenticeship learning.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Subject-driven text-to-image generation via apprenticeship learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.350474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 97cea038-f983-41e7-940d-3aa97d0d81fb · outbound

This paper cites APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.591219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a24acb78-f90c-4c57-8a85-f69b3c00c8d7 · outbound

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

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.338456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 42e3ffe7-f1d6-415c-80b7-2de87db47136 · outbound

This paper cites Automatic classification of defective photovoltaic module cells in electrolu- minescence images.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Automatic classification of defective photovoltaic module cells in electrolu- minescence images

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.327214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ddc032ed-c105-4d95-a4ed-e2977a46634d · outbound

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

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Anomaly detection via reverse distillation from one-class embedding

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.315811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7e721146-e979-4b75-88ed-9eea543b4d70 · outbound

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

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.303799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9907a2e7-dca8-46ff-819c-5b044dc195c7 · outbound

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

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Anomalygpt: Detecting industrial anomalies using large vision-language models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.291846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f6d61118-a6ae-433d-b99e-e346a75242e4 · outbound

This paper cites DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.615036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ae3c7ca-4891-4d40-a342-ea994e070e87 · outbound

This paper cites Denoising diffusion probabilistic models.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Denoising diffusion probabilistic models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.619307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7ebd9e2-6883-4c21-b14c-6a50d25c1bda · outbound

This paper cites Automated segmentation of macular edema in oct using deep neural networks.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Automated segmentation of macular edema in oct using deep neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.274249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation afb96ea3-3f9e-47dd-9139-c7458949a7cc · outbound

This paper cites Regis- tration based few-shot anomaly detection.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Regis- tration based few-shot anomaly detection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.262027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 94c7cf13-5d51-4465-ad40-1486c422efc4 · outbound

This paper cites Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-09T16:18:12.926645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 05817942-eb2d-47f2-a17d-277a4797775d · outbound

This paper cites Winclip: Zero-/few-shot anomaly classification and segmentation.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Winclip: Zero-/few-shot anomaly classification and segmentation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.634462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cb25220-6ab8-4764-80c1-5c6d3d0ccdd1 · outbound

This paper cites Identifying medical diagnoses and treatable diseases by image-based deep learning.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Identifying medical diagnoses and treatable diseases by image-based deep learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.240751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a14e04b9-b659-4ead-a10f-df2135f6f053 · outbound

This paper cites Cifar-10 (canadian institute for advanced research), 2010.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Cifar-10 (canadian institute for advanced research), 2010

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.228006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1374f6b2-d28f-4f0b-9df1-b86ebfa0c7d2 · outbound

This paper cites Gradient-based learning applied to document recognition.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Gradient-based learning applied to document recognition

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.645813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f90679a-caff-4738-a5b8-823770a43410 · outbound

This paper cites Cutpaste: Self-supervised learning for anomaly detection and localization.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Cutpaste: Self-supervised learning for anomaly detection and localization

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.650245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fbfa8d7-659f-4bbc-94bd-f3276fb46d96 · outbound

This paper cites Self- supervised anomaly detection, staging and segmentation for retinal images.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Self- supervised anomaly detection, staging and segmentation for retinal images

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.200028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f0a6449c-5d20-4731-81a3-e196ec8fc5f9 · outbound

This paper cites Classifier two sample test for video anomaly detections.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Classifier two sample test for video anomaly detections

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.187094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 23fa78e8-96b2-41c2-88fa-5a7a209343c4 · outbound

This paper cites Cones: Concept neurons in diffusion models for customized generation.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Cones: Concept neurons in diffusion models for customized generation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.174521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d0f09811-cab2-4e92-b7fc-eae0692b8d91 · outbound

This paper cites On Diffusion Modeling for Anomaly Detection.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection On Diffusion Modeling for Anomaly Detection

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.664793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68ac8398-6c11-4f79-bfae-76cefc46d5ec · outbound

This paper cites Boomerang: Local sampling on image manifolds using diffusion models.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Boomerang: Local sampling on image manifolds using diffusion models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.668969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63b2a1c1-5ffb-498d-a69e-7a0e8d53e3e4 · outbound

This paper cites Sdedit: Guided image synthesis and editing with stochastic differential equations.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Sdedit: Guided image synthesis and editing with stochastic differential equations

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.673073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfdb2f85-e62a-4dd5-af70-bd06decac207 · outbound

This paper cites Deep learning for anomaly detection: A review.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Deep learning for anomaly detection: A review

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.152121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5ac1b7e6-3c40-4504-9809-8038f21a5d52 · outbound

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

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Learning transferable visual models from natural language supervision

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.680620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b4c4a84-cdc6-4fc3-a875-452358c67d21 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection High-resolution image synthesis with latent diffusion models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.684379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed6c5a6f-5051-4dd4-a15f-05bead885b3f · outbound

This paper cites Towards total recall in industrial anomaly detection.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Towards total recall in industrial anomaly detection

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.122822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0cea28e8-0f52-4a3c-a616-d5f3b5e98f9e · outbound

This paper cites Dream- booth: Fine tuning text-to-image diffusion models for subject-driven generation.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Dream- booth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.109950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dcb63759-fce1-4954-9319-e58ac502db7e · outbound

This paper cites A public fabric database for defect detection methods and results.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection A public fabric database for defect detection methods and results

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.096594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 07f9ee23-1d53-4d47-973b-0a9ef2e8d8c8 · outbound

This paper cites Real-world anomaly detection in surveillance videos.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Real-world anomaly detection in surveillance videos

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.699255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76981237-4920-432c-8ba9-0e59802087db · outbound

This paper cites Segmentation-based deep-learning approach for surface-defect detection.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Segmentation-based deep-learning approach for surface-defect detection

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.074622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9af073cd-3dc1-4a6b-bcba-2f0d3f6cbaea · outbound

This paper cites Revisiting reverse distillation for anomaly detection.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Revisiting reverse distillation for anomaly detection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.062026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 020b6e22-d610-4f59-96f2-a8496bbbdb03 · outbound

This paper cites Few-shot fast-adaptive anomaly detection.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Few-shot fast-adaptive anomaly detection

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.048401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6071f1bf-580a-47c2-a26c-34857fda7148 · outbound

This paper cites Exploiting structural consistency of chest anatomy for unsupervised anomaly detection in radiography images.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Exploiting structural consistency of chest anatomy for unsupervised anomaly detection in radiography images

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.035049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0371c301-1a95-4a4e-bdd6-8cde0b99212a · outbound

This paper cites Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.720074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d139f4e9-b35d-41c8-8386-a189c3a34f86 · outbound

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

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Draem-a discriminatively trained reconstruction embedding for surface anomaly detection

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.022454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ac87e85c-fbcd-4775-9992-d23d486150b9 · outbound

This paper cites Reconstruction by inpainting for visual anomaly detection.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Reconstruction by inpainting for visual anomaly detection

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:13.009216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cc9b58d9-d17a-459e-a5c1-5fe71767f766 · outbound

This paper cites Expanding small-scale datasets with guided imagination.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Expanding small-scale datasets with guided imagination

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:12.996425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6f06d67c-ea13-4c2e-9a80-29a15f2b4bfc · outbound

This paper cites Conditional prompt learning for vision- language models.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Conditional prompt learning for vision- language models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.735932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3059a1d-3e39-4616-8817-bcb50ea885b6 · outbound

This paper cites Encoding structure-texture relation with p-net for anomaly detection in retinal images.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Encoding structure-texture relation with p-net for anomaly detection in retinal images

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:12.976261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6c4bc015-38d9-4e2a-8bea-7c49c72e3c34 · outbound

This paper cites Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:12.743964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9174525b-60d0-4621-a490-9c9e091b43b8 · outbound

This paper cites Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-09T16:18:12.791496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 371850ea-6224-4941-904e-40ccbc92038b · outbound

This paper cites [o] without flaw.

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection [o] without flaw

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:18:12.964036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.