Pith. sign in

Paper Citation Record · LEDGER

Classification Accuracy Score for Conditional Generative Models

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1905.10887.

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

pith.paper-citation-record.v1
1905.10887 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T16:19:07.559228Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T22:23:48.013248Z

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 bef0d659-e705-43c2-920c-45cde97ffae9 · inbound

A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark cites this paper.

A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark Classification Accuracy Score for Conditional Generative Models

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-17T22:12:05.809185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-17T22:12:05.731960Z digest=sha256:b9520d08d0c3320da11e7a798291658a9c740a22291b0b6b05dc6168bcfcc8b1

Observation b61239fe-3fb5-462a-b67b-393418f861bd · inbound

Improved Denoising Diffusion Probabilistic Models cites this paper.

Improved Denoising Diffusion Probabilistic Models Classification Accuracy Score for Conditional Generative Models

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-16T19:19:14.980290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-16T19:19:14.899966Z digest=sha256:9ac014b8556822af1ccaf930c4dd7ffdf66016b5d648d93d976fac90e1cd8086

Observation 24b824a8-373c-473b-bb3d-9f0bdc991535 · inbound

Concordia: Self-Improving Synthetic Tables for Federated LLMs cites this paper.

Concordia: Self-Improving Synthetic Tables for Federated LLMs Classification Accuracy Score for Conditional Generative Models

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:16:27.530011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-12T04:26:50.410397Z digest=sha256:64500e8bce148ed94e74ec16f829d454f8b11b69057a66ea9c82a337ff7ff41b

Observation cc95c3f0-75dd-44ae-a325-0dafc0ba7890 · inbound

Concordia: Self-Improving Synthetic Tables for Federated LLMs cites this paper.

Concordia: Self-Improving Synthetic Tables for Federated LLMs Classification Accuracy Score for Conditional Generative Models

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-20T22:23:48.016355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-20T22:21:03.637418Z digest=sha256:aed40f7e01b465002238ef7ccf3389c59e0bfacf26129abaef2a3bd827167786

Observation 459c54f7-354d-4741-b688-693eca2c5291 · inbound

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift cites this paper.

SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift Classification Accuracy Score for Conditional Generative Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-01T16:19:07.559228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T16:19:07.559228Z digest=sha256:4ad79cbca31000b986bea2165cbc720214b326eaf2c2187881d7ac6a2a99e213