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Paper Citation Record · LEDGER

Do Generated Data Always Help Contrastive Learning?

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2403.12448.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2403.12448 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T08:12:18.373103Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T22:57:26.185636Z

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 e1f8b234-50ae-4a35-a057-d5b760ef3c42 · inbound

SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization cites this paper.

SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization Do Generated Data Always Help Contrastive Learning?

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:16:28.838582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T03:33:45.192139Z digest=sha256:3bf356dfb80df9a16e0406035c76343a042fd023546af69f92347c2bb7187b9e

Observation d8ca53ac-85a2-444c-bfa2-b31697b64a25 · inbound

A Theoretical Analysis of Memory and Overfitting Phenomena in Stochastic Interpolation Models cites this paper.

A Theoretical Analysis of Memory and Overfitting Phenomena in Stochastic Interpolation Models Do Generated Data Always Help Contrastive Learning?

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-07-02T22:57:26.187029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-27T18:35:15.196183Z digest=sha256:b20ecbef334936634c31c8822fe0cf5fe79d827c730368f068662a9ee50c25cf

Observation 40335866-76d5-4e75-a5ad-f1c1cf3dd289 · inbound

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting cites this paper.

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting Do Generated Data Always Help Contrastive Learning?

Reference 33

Resolution
unresolved
no resolver link, observed 2026-07-12T08:12:18.373103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T08:12:18.373103Z digest=sha256:162bae1b4fd14cfdfbb76d99778c8fdac0beea39eb05867bcd0cf1feab8def86