Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Generative Adversarial Networks: An Overview
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1710.07035.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:30:10.420988Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 887f2fb6-7a9a-4b53-a803-1030f0cc068a · inbound
Mal-D2GAN: Double-Detector based GAN for Malware Generation cites this paper.
Mal-D2GAN: Double-Detector based GAN for Malware Generation Generative Adversarial Networks: An Overview
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:30:10.420988Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d8e6fc3-896d-45cf-be79-119c90a15681 · inbound
Case Studies of Generative Machine Learning Models for Dynamical Systems cites this paper.
Case Studies of Generative Machine Learning Models for Dynamical Systems Generative Adversarial Networks: An Overview
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T00:01:46.845505Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 40f3572f-9f62-421d-8c5e-0cdc4dfd1630 · inbound
Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models cites this paper.
Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models Generative Adversarial Networks: An Overview
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T22:32:54.039241Z
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.