Paper Citation Record · LEDGER

Anomaly detection using Diffusion-based methods

As of 17 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2412.07539.

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

pith.paper-citation-record.v1
2412.07539 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:47:46.565365Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

36 of 36 outbound references displayed

verified exact3
verified fuzzy28
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4395a9f1-7c6a-4d4e-9010-c4b626c3db9d · outbound

This paper cites Isolation f orest.

Anomaly detection using Diffusion-based methods Isolation f orest

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.963900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f4eb785c-5428-4192-ba51-f706debd1b58 · outbound

This paper cites Estimating the support of a high-dimensional d istribution.

Anomaly detection using Diffusion-based methods Estimating the support of a high-dimensional d istribution

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.955579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ca4e51a9-d38d-411f-9492-f7d2a515b44f · outbound

This paper cites Copod: Copula-based outlier detection.

Anomaly detection using Diffusion-based methods Copod: Copula-based outlier detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.946858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0148eb54-2e66-40b0-b0f3-532881e11e17 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Anomaly detection using Diffusion-based methods Denoising Diffusion Probabilistic Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T18:47:46.473276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a72bd70-eb6e-4242-9e9b-8b410207ebe7 · outbound

This paper cites Denoising Diffusion Implicit Models.

Anomaly detection using Diffusion-based methods Denoising Diffusion Implicit Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T18:47:46.476703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59cd0a3c-f72a-4702-af5b-c3434b34a65e · outbound

This paper cites Semi-supervised anomaly detection through self-supervised learning and fe ature reﬁnement.

Anomaly detection using Diffusion-based methods Semi-supervised anomaly detection through self-supervised learning and fe ature reﬁnement

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.938645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1022c596-e51b-4fcf-ba83-65b7b879a9c5 · outbound

This paper cites Bagging-randomminer: A one-class classiﬁer for ﬁle access-based masquerade detec tion.

Anomaly detection using Diffusion-based methods Bagging-randomminer: A one-class classiﬁer for ﬁle access-based masquerade detec tion

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.929757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 63defaa5-5058-4b7a-b91a-35023dbf0463 · outbound

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

Anomaly detection using Diffusion-based methods Deep learning for anomaly detection: A review

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.922169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 91a37eef-ea1e-4a42-b820-ad00a82721c8 · outbound

This paper cites A unifying review of deep and shallow anomaly detection.

Anomaly detection using Diffusion-based methods A unifying review of deep and shallow anomaly detection

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.914724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eabcd0c2-a692-45b2-80c1-ef13c02740b8 · outbound

This paper cites V andermeulen, Nico Görnitz, et al.

Anomaly detection using Diffusion-based methods V andermeulen, Nico Görnitz, et al

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.906526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 39a17fde-9ecf-4165-bc20-fe7e3bb6a30d · outbound

This paper cites Deep autoencoding gau ssian mixture model for un- supervised anomaly detection.

Anomaly detection using Diffusion-based methods Deep autoencoding gau ssian mixture model for un- supervised anomaly detection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.898281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation aa9106ee-e500-4bd7-b01f-eb8dd911887b · outbound

This paper cites Contrastive learning for semi-supervised anomaly detection.

Anomaly detection using Diffusion-based methods Contrastive learning for semi-supervised anomaly detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.889889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8bdc9839-13cb-43ee-8c92-9bfa9ce7302a · outbound

This paper cites Anomaly detection wi th robust deep autoencoders.

Anomaly detection using Diffusion-based methods Anomaly detection wi th robust deep autoencoders

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.881231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 11216084-d9f1-432d-9f9d-f1d0fbb8e939 · outbound

This paper cites Drocc : Deep robust one-class classiﬁca- tion.

Anomaly detection using Diffusion-based methods Drocc : Deep robust one-class classiﬁca- tion

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.872992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f4f25b79-363f-4d20-8c6b-c9136ddf48e6 · outbound

This paper cites Classiﬁcation-based a nomaly detection for general data.

Anomaly detection using Diffusion-based methods Classiﬁcation-based a nomaly detection for general data

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.864406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 783bffad-8920-4465-afd1-cf3a8061a631 · outbound

This paper cites Lunar: Unifying local outlier detection methods via graph neural networks.

Anomaly detection using Diffusion-based methods Lunar: Unifying local outlier detection methods via graph neural networks

Reference 16

Resolution: verified exact; doi, observed 2026-08-11T18:47:46.593816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6d84837c-f170-4305-88f4-8e67ef2f80d9 · outbound

This paper cites International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines.

Anomaly detection using Diffusion-based methods International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-11T18:47:46.710417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4dfac1fe-9b22-4b0a-bc0d-6132b3321209 · outbound

This paper cites Ordisco: Effective and efﬁcient usage of incremental unlabeled data for semi-supervised continual learn- ing.

Anomaly detection using Diffusion-based methods Ordisco: Effective and efﬁcient usage of incremental unlabeled data for semi-supervised continual learn- ing

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.856046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 699f8c22-938a-48f4-a57f-66684a880ff9 · outbound

This paper cites V ariational autoencoder ba sed anomaly detection using recon- struction probability.

Anomaly detection using Diffusion-based methods V ariational autoencoder ba sed anomaly detection using recon- struction probability

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.847613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 60998625-e21a-40d6-b0f2-3685dd5c531c · outbound

This paper cites Anomaly detection us ing autoencoders with nonlinear dimensionality reduction.

Anomaly detection using Diffusion-based methods Anomaly detection us ing autoencoders with nonlinear dimensionality reduction

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.838961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 999290a9-535a-4eea-ae36-766d4b83cead · outbound

This paper cites Learning from privil eged information for anomaly detec- tion.

Anomaly detection using Diffusion-based methods Learning from privil eged information for anomaly detec- tion

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.830527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 240d42e5-5282-4de2-8647-719a1b15897f · outbound

This paper cites Generative adversarial active learning for unsupervised outlier dete ction.

Anomaly detection using Diffusion-based methods Generative adversarial active learning for unsupervised outlier dete ction

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.822391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e3da80dd-c87b-439c-85dc-3ea92a1b7d57 · outbound

This paper cites Exact solutions for time-dependent complex symmetric potential well.

Anomaly detection using Diffusion-based methods Exact solutions for time-dependent complex symmetric potential well

Reference 23

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T18:47:46.698355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 86d0aded-5f3f-4ff2-bafb-33fdcbd6ccca · outbound

This paper cites Diffusion models for anomaly detection and repair.

Anomaly detection using Diffusion-based methods Diffusion models for anomaly detection and repair

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.814383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a5f21eb-65f6-4537-bc65-2cc5631d2e94 · outbound

This paper cites Anoddp m: Anomaly detection with denois- ing diffusion probabilistic models.

Anomaly detection using Diffusion-based methods Anoddp m: Anomaly detection with denois- ing diffusion probabilistic models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.806358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 42a85c0c-14f7-4412-b76b-9ac838867c68 · outbound

This paper cites Tim e-series anomaly detection using ddpm-based reconstruction.

Anomaly detection using Diffusion-based methods Tim e-series anomaly detection using ddpm-based reconstruction

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.797770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a7238767-d4a2-4588-8dbb-fbca9e60c938 · outbound

This paper cites Checkerboard di ffusion models for anomaly detec- tion via image in-painting.

Anomaly detection using Diffusion-based methods Checkerboard di ffusion models for anomaly detec- tion via image in-painting

Reference 27

Resolution: verified exact; raw_fallback, observed 2026-08-11T18:47:46.686985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f8d4db18-4de4-4a19-953d-4cc1ee2710db · outbound

This paper cites Deep generative image models using a laplacian pyram id of adversarial networks.

Anomaly detection using Diffusion-based methods Deep generative image models using a laplacian pyram id of adversarial networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.789265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1bbd29d6-ba17-4079-aee3-faff09e48883 · outbound

This paper cites Denoising di ffusion probabilistic models.

Anomaly detection using Diffusion-based methods Denoising di ffusion probabilistic models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.780495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a67ccf6b-16f6-40a7-b3d0-a219faa6eb18 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmentation.

Anomaly detection using Diffusion-based methods U- net: Convolutional networks for biomedical image segmentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.771246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 970fd6ac-3e31-4721-b364-697b578cc0ce · outbound

This paper cites Dis- criminative unsupervised feature learning with exemplar c onvolutional neural networks.

Anomaly detection using Diffusion-based methods Dis- criminative unsupervised feature learning with exemplar c onvolutional neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.762542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 87a406ae-6bce-43a9-ae1a-bc124b499503 · outbound

This paper cites Enhancement of the flow of vibrated grains through narrow apertures by addition of small particles.

Anomaly detection using Diffusion-based methods Enhancement of the flow of vibrated grains through narrow apertures by addition of small particles

Reference 32

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T18:47:46.616412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 92b9708a-430b-4b1d-a890-5df06f6bfd3f · outbound

This paper cites Auto-encoding variational b ayes.

Anomaly detection using Diffusion-based methods Auto-encoding variational b ayes

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.753705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2f7b0bbd-f755-4073-ad16-3367b98a1e2c · outbound

This paper cites Improved deno ising diffusion probabilistic mod- els.

Anomaly detection using Diffusion-based methods Improved deno ising diffusion probabilistic mod- els

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.744650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cf49160f-ba64-415e-96e7-2516e968f117 · outbound

This paper cites Diad: A diffusion-based fr amework for multi-class anomaly detection, 2023.

Anomaly detection using Diffusion-based methods Diad: A diffusion-based fr amework for multi-class anomaly detection, 2023

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T18:47:46.735771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 88065759-2768-4a03-90bd-062082a6abe7 · outbound

This paper cites On Diffusion Modeling for Anomaly Detection.

Anomaly detection using Diffusion-based methods On Diffusion Modeling for Anomaly Detection

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T18:47:46.565365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.