Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:1711.06969.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-24T20:48:51.430219Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-24T20:49:54.531120Z
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 8bb5ce35-4329-4375-b408-9e30013f7dc2 · inbound
How much real data do we actually need: Analyzing object detection performance using synthetic and real data cites this paper.
How much real data do we actually need: Analyzing object detection performance using synthetic and real data Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
Reference 11
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-24T20:49:54.536394Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.