Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning from Simulated and Unsupervised Images through Adversarial Training
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1612.07828.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T15:18:35.549689Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T15:10:47.536017Z
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 4714b7b9-db16-443f-aff2-0cd77591c787 · inbound
Seeding the Singularity for A.I cites this paper.
Seeding the Singularity for A.I Learning from Simulated and Unsupervised Images through Adversarial Training
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T15:18:35.549689Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 04ac034d-27da-4043-9815-a5c26a91c00a · inbound
Beyond Photo Realism for Domain Adaptation from Synthetic Data cites this paper.
Beyond Photo Realism for Domain Adaptation from Synthetic Data Learning from Simulated and Unsupervised Images through Adversarial Training
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:06:15.623820Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1cd832f5-876e-44ea-8c3a-631448a7e717 · inbound
Learning more with the same effort: how randomization improves the robustness of a robotic deep reinforcement learning agent cites this paper.
Learning more with the same effort: how randomization improves the robustness of a robotic deep reinforcement learning agent Learning from Simulated and Unsupervised Images through Adversarial Training
Reference 35
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T15:10:47.540929Z
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.