Paper Citation Record · LEDGER

Deep Learning for Anomaly Detection: A Review

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2007.02500.

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

pith.paper-citation-record.v1
2007.02500 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:51:08.056121Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T16:55:08.359045Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 90728b9d-ffc4-4c97-a5fb-43ba49479941 · inbound

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series cites this paper.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Deep Learning for Anomaly Detection: A Review

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:08.056121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 188d3b71-99c9-464c-ac57-679932f3d452 · inbound

We Need to Rethink Benchmarking in Anomaly Detection cites this paper.

We Need to Rethink Benchmarking in Anomaly Detection Deep Learning for Anomaly Detection: A Review

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:29.771256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6926be2-4c52-4fa4-80d1-d3025326f2e0 · inbound

Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection cites this paper.

Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection Deep Learning for Anomaly Detection: A Review

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:56:00.540924Z

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 a7d1a5cf-799f-453d-b162-804e2646fdd1 · inbound

Benchmark AUC Is Not Deployable Reliability: A Cross-Dataset Audit of Off-the-Shelf Features for Surveillance Video Anomaly Detection cites this paper.

Benchmark AUC Is Not Deployable Reliability: A Cross-Dataset Audit of Off-the-Shelf Features for Surveillance Video Anomaly Detection Deep Learning for Anomaly Detection: A Review

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T07:34:21.598703Z

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 00e4ab8c-ac31-45b8-8f4b-dfd8a84d25d5 · inbound

Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems cites this paper.

Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems Deep Learning for Anomaly Detection: A Review

Reference 26

Resolution: metadata mismatch; local_arxiv, observed 2026-07-08T16:55:08.360325Z

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.