Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.14584.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:03:27.739980Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T04:36:40.118694Z
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 868fa835-a716-4f6f-9411-8aee9c97ffde · inbound
AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks cites this paper.
AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:03:27.739980Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 433d50c3-d7fd-43d3-805c-571e988dfd79 · inbound
Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Survey cites this paper.
Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Survey DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning
Reference 133
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T04:36:40.124650Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.