Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A survey of synthetic data augmentation methods in computer vision
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2403.10075.
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 2026-08-05T05:50:59.318364Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-12T07:31:26.228096Z
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 4d76867a-52f9-40e0-9b51-7d2a4de4e371 · inbound
SynGen-Vision: Synthetic Data Generation for training industrial vision models cites this paper.
SynGen-Vision: Synthetic Data Generation for training industrial vision models A survey of synthetic data augmentation methods in computer vision
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T05:50:59.318364Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 18c21661-319e-4c05-a3be-4c9322e27b53 · inbound
Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning cites this paper.
Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning A survey of synthetic data augmentation methods in computer vision
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T11:05:08.482583Z
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.
Observation b33a55fc-0ca6-4dc1-832a-474a555ee742 · inbound
AtteConDA: Attention-Based Conflict Suppression in Multi-Condition Diffusion Models and Synthetic Data Augmentation cites this paper.
AtteConDA: Attention-Based Conflict Suppression in Multi-Condition Diffusion Models and Synthetic Data Augmentation A survey of synthetic data augmentation methods in computer vision
Reference 55
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T07:31:26.237349Z
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.