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Paper Citation Record · LEDGER

Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks

As of 16 August 2026, this Paper Citation Record lists 3 of 3 outbound references and 0 inbound Pith citation observations for arXiv:1908.08331.

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

pith.paper-citation-record.v1
1908.08331 v2

Coverage vector

measured 3 of 3 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:46:46.289660Z

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

3 of 3 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1683ee35-f61e-4780-a6b9-e6554c06c5e6 · outbound

This paper cites https://doi.org/10.1109/CVPR.2017.65.

Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks https://doi.org/10.1109/CVPR.2017.65

Reference 549

Resolution
verified exact
doi, observed 2026-08-14T11:46:46.323327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:46:46.280637Z digest=sha256:a039a06f60fb2d6dd8795da63e84e05fba78e02cd8b7eeb340b4a7ca8d903179

Observation d86df3ce-a491-421f-860c-7e4583cba982 · outbound

This paper cites Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications.

Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications

Reference 3677

Resolution
metadata mismatch
local_arxiv, observed 2026-08-14T11:46:46.353084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:46:46.285009Z digest=sha256:c0623a1a90d824740e0f46d2ddca5d1b7f8ba79e01565a8183a0d3771cc1282e

Observation 4bf70a90-f354-4ffe-8c7f-7bd04e27ca05 · outbound

This paper cites What do different evaluation metrics tell us about saliency models?.

Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks What do different evaluation metrics tell us about saliency models?

Reference 3805

Resolution
unresolved
no resolver link, observed 2026-08-14T11:46:46.289660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:46:46.289660Z digest=sha256:642b0241f486b54911b8a0900226a43ab37c49089ddcdde31faff62d55847e50

Pith citing papers

No inbound Pith citation observations are available.