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Paper Citation Record · LEDGER

A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2302.01217.

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

pith.paper-citation-record.v1
2302.01217 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:11:04.698961Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T15:54:48.817866Z

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 88c8d12d-9d51-4c0e-b445-f26c682ed8cf · inbound

A Survey on Diffusion Models for Inverse Problems cites this paper.

A Survey on Diffusion Models for Inverse Problems A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models

Reference 125

Resolution
verified exact
arxiv_id, observed 2026-05-17T04:31:49.362805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-17T04:31:49.105271Z digest=sha256:21169fc43d04ede4df2f7bcb7d51213c6a8123a1b9628206b9deaf514dc7e95f

Observation 8cd72d3e-e8e2-423a-80f6-a7a498d9a615 · inbound

SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis cites this paper.

SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models

Reference 91

Resolution
unresolved
no resolver link, observed 2026-08-12T13:11:04.698961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:11:04.698961Z digest=sha256:917b45474fbdc1dee1332b14ff4c9d68b2e0cbb4803a480a6a99f758bebfb5e6

Observation 55ea9ba9-885c-4825-a6bf-e66d129745ae · inbound

Generative diffusion models for spatiotemporal influenza forecasting cites this paper.

Generative diffusion models for spatiotemporal influenza forecasting A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T21:51:19.760568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-08T04:08:29.810085Z digest=sha256:ccdaa52242a6ffb46795287192b6aaaa97062c0295e47e394e3ccea54810455e

Observation 51944ec5-abdc-4492-a360-55e61a151430 · inbound

Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications cites this paper.

Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-06-30T15:54:48.819361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-30T15:52:48.751172Z digest=sha256:83c97235ddb21c78dae65508462027dd3d3f32c8596179bd01f75c16e331f1d4