Paper Citation Record · LEDGER

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling

As of 19 July 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2602.23013.

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

pith.paper-citation-record.v1
2602.23013 v3

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-15T18:48:08.212050Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-19T06:30:13.599613+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T05:22:43.072750Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-20T05:23:03.575924Z

Reference resolution

43 of 43 outbound references displayed

verified exact9
verified fuzzy31
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cc7c5d90-5e13-4d1e-9ec9-b8a6729af2ac · outbound

This paper cites Ganomaly: Semi-supervised anomaly detection via adversarial training.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Ganomaly: Semi-supervised anomaly detection via adversarial training

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.647843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation fad4404c-580d-4896-bb60-a8ac89486d3b · outbound

This paper cites Deep Nearest Neighbor Anomaly Detection.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Deep Nearest Neighbor Anomaly Detection

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:16.770480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation adcac488-ebc9-4896-9544-00ee25d24948 · outbound

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

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-15T18:50:16.753205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation e626bc69-8fa3-4367-aa05-317ae4d1bbd5 · outbound

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

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.680578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation b114efc7-8054-456f-a38a-9433805e1e4e · outbound

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

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.671461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation f34c99e7-745a-4265-8649-5cd660458f80 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Emerging properties in self-supervised vision transformers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.715005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 9abe522c-4927-43a1-adb0-051160e72561 · outbound

This paper cites Anomaly detection: A survey.ACM computing surveys (CSUR), 41(3):1–58.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Anomaly detection: A survey.ACM computing surveys (CSUR), 41(3):1–58

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.719795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation a73b9523-f013-4ea8-8081-2403b47d28ea · outbound

This paper cites A simple framework for contrastive learning of visual representations.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling A simple framework for contrastive learning of visual representations

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.723964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation ee783186-5cff-4e24-a598-f3d374b014d6 · outbound

This paper cites an unresolved cited work.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Unresolved cited work

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.688534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation d4f9730d-2ec0-4d22-a16c-367db87b21ac · outbound

This paper cites Sub-Image Anomaly Detection with Deep Pyramid Correspondences.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:16.759266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation b1fa1039-13bb-43a3-a426-256de8a7e8d4 · outbound

This paper cites Anomalydino: Boosting patch-based few-shot anomaly detection with dinov2.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Anomalydino: Boosting patch-based few-shot anomaly detection with dinov2

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.710360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation f88fcae3-9b69-4890-aee2-3011b49f718e · outbound

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

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.727857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation a8be3f87-64db-4594-8bfa-eed26a09c8ef · outbound

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

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Anomaly detection via reverse distillation from one-class embedding

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.706717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation b402ebe2-259f-4891-8070-d5c0db80d72f · outbound

This paper cites Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:16.765442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation fe1c36a1-37c9-468d-9957-b75e10156ef9 · outbound

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

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Fastrecon: Few-shot industrial anomaly detection via fast feature reconstruction

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.732590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 39cb4298-0c0a-4d54-b154-90bc2fda7a61 · outbound

This paper cites Transfusion–a transparency-based diffusion model for anomaly detection.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Transfusion–a transparency-based diffusion model for anomaly detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.749763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 57446235-f000-4bec-9aee-6a7aa147ce8f · outbound

This paper cites Masked autoencoders are scalable vision learners.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Masked autoencoders are scalable vision learners

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.741484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 1893866a-1678-4481-9583-7bad9823f03a · outbound

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

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Winclip: Zero-/few-shot anomaly classification and segmentation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.745552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 86c729c7-4e43-417b-85e5-d3df4c13476d · outbound

This paper cites Few-shot anomaly detection via personalization.IEEE Access, 12:11035–11051.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Few-shot anomaly detection via personalization.IEEE Access, 12:11035–11051

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.684507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation dbfe2174-c15b-4cfd-9e93-25f9b648b774 · outbound

This paper cites Zero-shot anomaly detection via batch normalization.Advances in Neural Information Processing Systems, 36:40963–40993.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Zero-shot anomaly detection via batch normalization.Advances in Neural Information Processing Systems, 36:40963–40993

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.737104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation acc8b4c2-730c-48cf-b3fa-367bd83c8ed2 · outbound

This paper cites Multimodal foundation models: From specialists to general-purpose assistants.Foundations and Trends® in Computer Graphics and Vision, 16(1-2):1–214.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Multimodal foundation models: From specialists to general-purpose assistants.Foundations and Trends® in Computer Graphics and Vision, 16(1-2):1–214

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.702638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 943cff89-2e84-460d-98f1-887798a80437 · outbound

This paper cites Musc: Zero-shot industrial anomaly classification and segmentation with mutual scoring of the unlabeled images.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Musc: Zero-shot industrial anomaly classification and segmentation with mutual scoring of the unlabeled images

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.776933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 755adc98-f5e6-4dc9-98e6-34d5b9fa6880 · outbound

This paper cites Promptad: Learning prompts with only normal samples for few-shot anomaly detection.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Promptad: Learning prompts with only normal samples for few-shot anomaly detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.666835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 1941f2dd-72c7-413a-96af-62d7466d9113 · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.675981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 3037d310-2ddf-4e6d-be53-98b10ec4111b · outbound

This paper cites One-for-all few-shot anomaly detection via instance-induced prompt learning.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling One-for-all few-shot anomaly detection via instance-induced prompt learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.697719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 6a43cb55-e8f2-4250-a48e-3c94b24b5ffc · outbound

This paper cites Principal components analysis (pca).Computers & Geosciences, 19 (3):303–342.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Principal components analysis (pca).Computers & Geosciences, 19 (3):303–342

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.652708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 0f9b2757-94b2-404b-acc1-aad8ff41e0a6 · outbound

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

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling DINOv2: Learning Robust Visual Features without Supervision

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-15T18:50:16.742060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 83a9d870-9c19-45da-975f-da6468f2974e · outbound

This paper cites Deep learning for anomaly detection: A review.ACM computing surveys (CSUR), 54(2):1–38.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Deep learning for anomaly detection: A review.ACM computing surveys (CSUR), 54(2):1–38

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.662371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation a1d1042e-1e7e-48b4-8519-d757aa85bbf3 · outbound

This paper cites an unresolved cited work.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Unresolved cited work

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.756827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 6f2d3959-1b3d-4552-9c4e-dda6f1818c4d · outbound

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

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Learning transferable visual models from natural language supervision

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.760427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 1e2094a8-55d0-425f-8cbf-b6c323bf7637 · outbound

This paper cites Towards total recall in industrial anomaly detection.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Towards total recall in industrial anomaly detection

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.764552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 072a18b2-ce0c-4098-8c96-dab2b05b94e5 · outbound

This paper cites Optimizing PatchCore for Few/many-shot Anomaly Detection.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Optimizing PatchCore for Few/many-shot Anomaly Detection

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T18:50:16.736925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 466ae972-ee6d-4384-a59f-280b8216cebe · outbound

This paper cites Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Reference 33

Resolution: metadata mismatch; local_arxiv, observed 2026-05-15T18:50:16.747591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation a41d8490-4a5e-46cb-908c-8cc47c229d50 · outbound

This paper cites f-anogan: Fast unsupervised anomaly detection with generative adversarial networks.Medical image analysis, 54:30–44.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling f-anogan: Fast unsupervised anomaly detection with generative adversarial networks.Medical image analysis, 54:30–44

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.768777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 011fa3d9-9d47-4cf1-8738-30de65884ee3 · outbound

This paper cites A novel anomaly detection scheme based on principal component classifier.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling A novel anomaly detection scheme based on principal component classifier

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.772721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 41aeb9b8-c3fb-4927-bdf1-1369a9e06810 · outbound

This paper cites DINOv3.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling DINOv3

Reference 36

Resolution: metadata mismatch; local_arxiv, observed 2026-05-15T18:50:16.775488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 91cfbcdd-5852-4bdb-90e4-67196f73b2c1 · outbound

This paper cites Probabilistic principal component analysis.Journal of the Royal Statistical Society Series B: Statistical Methodology, 61(3): 611–622.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Probabilistic principal component analysis.Journal of the Royal Statistical Society Series B: Statistical Methodology, 61(3): 611–622

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.753483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 017d8636-7f21-41b8-9a55-b42ead7adf9c · outbound

This paper cites Principal component analysis — Wikipedia, the free encyclopedia.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Principal component analysis — Wikipedia, the free encyclopedia

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.657143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation c3a60db1-d478-4494-9f26-72276cbc68f0 · outbound

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

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:16.731370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 5cb5653c-3e60-41b3-a152-09ca9e561992 · outbound

This paper cites Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:16.720071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation d918f5ba-6bd7-4126-8543-db4743686bb0 · outbound

This paper cites FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:16.725251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 0846eefa-8e11-4d47-8661-71d6b7abdbad · outbound

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

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Anomalyclip: Object-agnostic prompt learn- ing for zero-shot anomaly detection

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:16.713932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation bb1c8cf1-e287-405e-bb95-8de7fea86bf7 · outbound

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

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Spot-the-difference self-supervised pre-training for anomaly detection and segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T19:11:31.693164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Pith citing papers

Observation ae271d7e-1146-4b30-a44f-9a4ef4747e2d · inbound

Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detection cites this paper.

Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detection SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-20T05:23:03.577263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.