Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1610.06756.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T10:33:39.030729Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T15:19:28.087097Z
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 66c8946d-db10-4fd6-bd3b-fef4e8bdbd01 · inbound
A Possible Reason for why Data-Driven Beats Theory-Driven Computer Vision cites this paper.
A Possible Reason for why Data-Driven Beats Theory-Driven Computer Vision Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:33:39.030729Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 575405ef-8b33-45d8-ab56-c7b6c52c3891 · inbound
SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks cites this paper.
SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
Reference 71
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T15:19:28.124268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.