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Paper Citation Record · LEDGER

Metrics for saliency map evaluation of deep learning explanation methods

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2201.13291.

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

pith.paper-citation-record.v1
2201.13291 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T23:32:35.435673Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T16:07:41.074739Z

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 ad030959-148e-476c-bc81-e6214701abc0 · inbound

From Weight Perturbation to Feature Attribution for Explaining Fully Connected Neural Networks cites this paper.

From Weight Perturbation to Feature Attribution for Explaining Fully Connected Neural Networks Metrics for saliency map evaluation of deep learning explanation methods

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-19T16:07:41.076840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T16:06:05.508610Z digest=sha256:6e2ce4b4110ae7a3a3ca0147d974bfb66441f5140fe3fbf8a6ffd4da23d389f3

Observation 8089504d-a27a-4ab3-8349-b64892659669 · inbound

qZACH-ViT: Quantization-Aware Intrinsic Explanations with Recursive Attribution-Stabilized Optimization cites this paper.

qZACH-ViT: Quantization-Aware Intrinsic Explanations with Recursive Attribution-Stabilized Optimization Metrics for saliency map evaluation of deep learning explanation methods

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-01T23:32:35.435673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T23:32:35.435673Z digest=sha256:f806cf56137cefd459231536141f7ceb617a9fb97dbe7cc65ad4641ac903a5fd