Paper Citation Record · LEDGER

RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2403.05897.

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

pith.paper-citation-record.v1
2403.05897 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:37:40.089004Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T00:17:28.748158Z

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 0e2f3c1f-36a1-4f47-95ac-eb4b6e514672 · inbound

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data cites this paper.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:40.089004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56404d13-facc-4790-8131-50ccde887817 · inbound

Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection cites this paper.

Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T05:22:39.299225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9482b104-3d66-4d18-8165-c72283964a27 · inbound

Multi-View Pose-Agnostic Change Localization with Zero Labels cites this paper.

Multi-View Pose-Agnostic Change Localization with Zero Labels RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T22:04:06.229094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bebdeb20-42eb-49e7-9823-915c240264dc · inbound

Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision cites this paper.

Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:17:28.749878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.