Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Training on Thin Air: Improve Image Classification with Generated Data
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2305.15316.
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 2026-08-06T23:12:17.288618Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T18:13:49.197094Z
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 5f61543f-5d64-42ca-b214-283fe58ade9e · inbound
SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation cites this paper.
SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Training on Thin Air: Improve Image Classification with Generated Data
Reference 134
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:12:17.288618Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 97141dff-f77c-4606-9e25-ab8659dd7663 · inbound
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery cites this paper.
Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery Training on Thin Air: Improve Image Classification with Generated Data
Reference 62
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-06T20:04:07.964738Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6597f7c7-69b2-4c23-9601-faae8d198671 · inbound
Beyond Objects: Contextual Synthetic Data Generation for Fine-Grained Classification cites this paper.
Beyond Objects: Contextual Synthetic Data Generation for Fine-Grained Classification Training on Thin Air: Improve Image Classification with Generated Data
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T07:54:53.089720Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e16e1959-7d92-441e-a0ad-db8fb24aee7b · inbound
Towards Controllable Image Generation through Representation-Conditioned Diffusion Models cites this paper.
Towards Controllable Image Generation through Representation-Conditioned Diffusion Models Training on Thin Air: Improve Image Classification with Generated Data
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T18:13:49.198648Z
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 64a7a392-e6e5-4735-a384-1e2a5a38a417 · inbound
Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting cites this paper.
Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Training on Thin Air: Improve Image Classification with Generated Data
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T08:12:18.373103Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.