Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Video to Video Generative Adversarial Network for Few-shot Learning Based on Policy Gradient
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.20657.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:47:36.028367Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T18:17:54.997308Z
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 080eedcd-92f3-4396-87eb-e2a819d30df3 · inbound
Improving GANs by leveraging the quantum noise from real hardware cites this paper.
Improving GANs by leveraging the quantum noise from real hardware Video to Video Generative Adversarial Network for Few-shot Learning Based on Policy Gradient
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:47:36.028367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 426bd102-ae35-4942-9039-7005013e153f · inbound
Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation cites this paper.
Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation Video to Video Generative Adversarial Network for Few-shot Learning Based on Policy Gradient
Reference 50
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T18:17:54.999617Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.