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Paper Citation Record · LEDGER

Corrupted Image Modeling for Self-Supervised Visual Pre-Training

As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2202.03382.

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

pith.paper-citation-record.v1
2202.03382 v2

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 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:06:31.728684Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T17:26:22.642980Z

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 93311a26-a4e7-4e66-b674-c2903da98d2c · inbound

Cross-View Completion Models are Zero-shot Correspondence Estimators cites this paper.

Cross-View Completion Models are Zero-shot Correspondence Estimators Corrupted Image Modeling for Self-Supervised Visual Pre-Training

Reference 23

Resolution
verified exact
local_arxiv, observed 2026-08-11T17:26:22.649221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T17:26:22.273689Z digest=sha256:9dcc9e58b8e820c65c134616806036a57b3b05e765ec998e01c482f845bddd70

Observation 81be44e8-64c7-4007-8d86-edd0af0c7fd0 · inbound

Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis cites this paper.

Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis Corrupted Image Modeling for Self-Supervised Visual Pre-Training

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-15T20:06:31.728684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:06:31.728684Z digest=sha256:370720a3c407eaf70b8f97843816a52c1ad31933787f51b3ed69e859a17e905e