Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2308.00994.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T05:11:55.988142Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T17:43:54.316314Z
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 9cde35b2-ee97-46f3-bdc4-496b1cd78ec5 · inbound
T2ID-CAS: Diffusion Model and Class Aware Sampling to Mitigate Class Imbalance in Neck Ultrasound Anatomical Landmark Detection cites this paper.
T2ID-CAS: Diffusion Model and Class Aware Sampling to Mitigate Class Imbalance in Neck Ultrasound Anatomical Landmark Detection SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T05:11:55.988142Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e471f688-1fee-4e16-91b7-78795466130f · inbound
LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance cites this paper.
LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:53:04.980967Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c4501c72-96f8-4d11-86b6-a345f98cb181 · inbound
SGD-Mix: Enhancing Domain-Specific Image Classification with Label-Preserving Data Augmentation cites this paper.
SGD-Mix: Enhancing Domain-Specific Image Classification with Label-Preserving Data Augmentation SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems
Reference 65
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:50:22.409094Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eedc11b9-3db2-4196-a176-da2257fd3311 · inbound
Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification cites this paper.
Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:50:22.209813Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f59b6978-a3fd-492d-b398-44301ed4d42d · inbound
Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering cites this paper.
Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems
Reference 75
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T17:43:54.321500Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.