Paper Citation Record · LEDGER

DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2012.07122.

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

pith.paper-citation-record.v1
2012.07122 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:10:08.767273Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:40:06.743654Z

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 3ac7d13a-da35-45b4-86dd-d6474f39c26f · inbound

Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localization cites this paper.

Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localization DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-24T06:46:02.995756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4d22d730-9193-4e16-9c37-32fa0c50fc26 · inbound

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology cites this paper.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:08.767273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ceb5afe1-121e-4ab7-8c86-3d6657628556 · inbound

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network cites this paper.

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T22:23:21.139575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0de807ff-8669-422c-96ac-e003203eeea5 · inbound

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation cites this paper.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:42.570931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e8fd7e4-1170-42a6-b686-c2cc81898f17 · inbound

Hypergraph Normal World Models for Logical Visual Anomaly Detection cites this paper.

Hypergraph Normal World Models for Logical Visual Anomaly Detection DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:06.745164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 14dadb67-d429-4812-b1d0-d8264f4b4806 · inbound

Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection cites this paper.

Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T07:54:21.763253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2bd6df22-10dc-42bc-94a0-787484993800 · inbound

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection cites this paper.

SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-02T01:53:17.488532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.