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Paper Citation Record · LEDGER

Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.09221.

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

pith.paper-citation-record.v1
2503.09221 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-01T05:33:22.798253Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T10:25:41.627287Z

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 a8db0834-8b69-449e-9089-8c0c73b7abb4 · inbound

A Real-Calibrated Synthetic-First Data Engine cites this paper.

A Real-Calibrated Synthetic-First Data Engine Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:26:26.743554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-12T04:14:56.374997Z digest=sha256:e173ab02fea61ea3f80c3bf15eb7d8d46f243fcd0f936b4cc07180cb3ce6c08d

Observation a096a28f-00a4-4a82-8ed0-a934bfb33059 · inbound

Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models cites this paper.

Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets

Reference 14

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T10:25:41.629046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-07-01T05:33:22.798253Z digest=sha256:3389a24265faef88aaec941ead10a0796a1f63bf62379c2b91897a501bb318f5