Paper Citation Record · LEDGER

Learning Multi-view Multi-class Anomaly Detection

As of 23 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2504.21294.

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

pith.paper-citation-record.v1
2504.21294 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:12:58.557196Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T20:16:06.916465Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T22:05:48.339193Z

Reference resolution

28 of 28 outbound references displayed

verified exact0
verified fuzzy24
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8ed3e439-9516-47f5-8666-a8b4235767bb · outbound

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

Learning Multi-view Multi-class Anomaly Detection Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:59.319833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 268fc390-2e06-4223-9af5-4ffc3d64ce55 · outbound

This paper cites Uninformed students: Student-teacher anomaly detection with discrimi- native latent embeddings.

Learning Multi-view Multi-class Anomaly Detection Uninformed students: Student-teacher anomaly detection with discrimi- native latent embeddings

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:59.299373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 07155f58-db55-4ee1-aac7-9a6d4a0f5582 · outbound

This paper cites Multi-view 3d object detection network for autonomous driving.

Learning Multi-view Multi-class Anomaly Detection Multi-view 3d object detection network for autonomous driving

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:59.283474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8619ff01-792e-4f4d-a4fc-0704a3da401e · outbound

This paper cites Vision Transformers Need Registers.

Learning Multi-view Multi-class Anomaly Detection Vision Transformers Need Registers

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T05:12:58.343099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49a308bf-1f8a-4ed3-bc33-f3effea52cd4 · outbound

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

Learning Multi-view Multi-class Anomaly Detection Anomaly detection via reverse distillation from one-class embedding

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:59.263748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c9ce3c7b-5092-4562-ac37-f9ea7ac82ccc · outbound

This paper cites Prioritized local matching network for cross-category few-shot anomaly detection.

Learning Multi-view Multi-class Anomaly Detection Prioritized local matching network for cross-category few-shot anomaly detection

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:59.241394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f0efe6f3-56ae-4685-b9df-0a3ef58fc515 · outbound

This paper cites Nng-mix: Improving semi-supervised anomaly detection with pseudo- anomaly generation.

Learning Multi-view Multi-class Anomaly Detection Nng-mix: Improving semi-supervised anomaly detection with pseudo- anomaly generation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:59.219093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7ad400a8-378f-44f7-b7b5-789f4356e4b2 · outbound

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

Learning Multi-view Multi-class Anomaly Detection Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:59.189430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b61e02ee-e6bb-480e-b865-2c9ba3354be4 · outbound

This paper cites Recon- trast: Domain-specific anomaly detection via contrastive reconstruction.

Learning Multi-view Multi-class Anomaly Detection Recon- trast: Domain-specific anomaly detection via contrastive reconstruction

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:59.164960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 090b8ca3-4925-4eca-9ff4-aace3daa7c83 · outbound

This paper cites Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection.

Learning Multi-view Multi-class Anomaly Detection Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T05:12:58.395204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f39c82f-2f28-4b23-b981-23bc1ef95dbe · outbound

This paper cites Mambaad: Exploring state space models for multi-class unsupervised anomaly detection.

Learning Multi-view Multi-class Anomaly Detection Mambaad: Exploring state space models for multi-class unsupervised anomaly detection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:59.135944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b915b310-7b41-47d5-9973-a47ad7e863f1 · outbound

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

Learning Multi-view Multi-class Anomaly Detection A diffusion- based framework for multi-class anomaly detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:59.096754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5aeb58f8-18ae-4b8f-b60e-eb1ffa156322 · outbound

This paper cites Learning Multi-view Anomaly Detection with Efficient Adaptive Selection.

Learning Multi-view Multi-class Anomaly Detection Learning Multi-view Anomaly Detection with Efficient Adaptive Selection

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T05:12:58.429685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0595dbf-fb60-460e-a499-0265dd7d0157 · outbound

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

Learning Multi-view Multi-class Anomaly Detection Cut- paste: Self-supervised learning for anomaly detection and localization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:59.061377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a271a4cf-fa0f-4d76-abed-6ae8077f82b3 · outbound

This paper cites Center- aware adversarial autoencoder for anomaly detection.

Learning Multi-view Multi-class Anomaly Detection Center- aware adversarial autoencoder for anomaly detection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:59.027738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0e6ac74e-a370-4058-a3af-430d78c21149 · outbound

This paper cites Anomaly detection on attributed networks via contrastive self- supervised learning.

Learning Multi-view Multi-class Anomaly Detection Anomaly detection on attributed networks via contrastive self- supervised learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:59.004910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d7f66249-a9d4-475e-a774-7ce589fca1ca · outbound

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

Learning Multi-view Multi-class Anomaly Detection Simplenet: A simple network for image anomaly detection and localization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:58.982926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 76626e7f-e158-4672-9ef5-08ea6350c9e7 · outbound

This paper cites Zoom in and out: A mixed-scale triplet network for camouflaged object detection.

Learning Multi-view Multi-class Anomaly Detection Zoom in and out: A mixed-scale triplet network for camouflaged object detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:58.953145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 66514bf9-5a57-40fd-8a5c-acef2818b80b · outbound

This paper cites Towards total recall in industrial anomaly detection.

Learning Multi-view Multi-class Anomaly Detection Towards total recall in industrial anomaly detection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:58.927777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 53208a42-cd89-4974-a280-16a328584a22 · outbound

This paper cites Multi-view convolutional neural networks for 3d shape recogni- tion.

Learning Multi-view Multi-class Anomaly Detection Multi-view convolutional neural networks for 3d shape recogni- tion

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:58.894317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f7c4baef-1e47-4bfe-b42d-ea02d5848a92 · outbound

This paper cites Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection.

Learning Multi-view Multi-class Anomaly Detection Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:58.875053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b5233a34-2e62-475a-8eeb-82ba7f12d246 · outbound

This paper cites Mvster: Epipolar transformer for efficient multi-view stereo.

Learning Multi-view Multi-class Anomaly Detection Mvster: Epipolar transformer for efficient multi-view stereo

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T05:12:58.501881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 169c8051-9e00-4d0d-9a4a-6f2a5b7d60ca · outbound

This paper cites Aide: A vision-driven multi-view, multi-modal, multi-tasking dataset for assistive driving perception.

Learning Multi-view Multi-class Anomaly Detection Aide: A vision-driven multi-view, multi-modal, multi-tasking dataset for assistive driving perception

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:58.842915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation abf9a468-a437-4dc5-86d4-610438bfb619 · outbound

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

Learning Multi-view Multi-class Anomaly Detection A unified model for multi-class anomaly detection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:58.826422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation deb2c456-adab-47d5-a037-8156fe105f8a · outbound

This paper cites Tf 2: Few-shot text-free training-free defect image generation for industrial anomaly inspection.

Learning Multi-view Multi-class Anomaly Detection Tf 2: Few-shot text-free training-free defect image generation for industrial anomaly inspection

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:58.806639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 98239542-ea8c-4064-a792-42051faff9a6 · outbound

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

Learning Multi-view Multi-class Anomaly Detection Draem-a discrimi- natively trained reconstruction embedding for surface anomaly detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:58.777146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b52faebf-96e6-4116-ba87-8a56e60492d8 · outbound

This paper cites Reconstruction by inpainting for visual anomaly detection.

Learning Multi-view Multi-class Anomaly Detection Reconstruction by inpainting for visual anomaly detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:58.742792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6aa52798-3d32-4a3a-9aba-9fd434426e63 · outbound

This paper cites Destseg: Segmentation guided denoising student-teacher for anomaly detection.

Learning Multi-view Multi-class Anomaly Detection Destseg: Segmentation guided denoising student-teacher for anomaly detection

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:12:58.711611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 7df5aec7-9e47-407b-b871-f96db9aa81b1 · inbound

SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection cites this paper.

SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection Learning Multi-view Multi-class Anomaly Detection

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:05:48.341230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.