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Paper Citation Record · LEDGER

Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2307.16889.

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

pith.paper-citation-record.v1
2307.16889 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:04:04.161211Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T22:45:49.560171Z

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 557e5d61-7484-4a53-a36f-b31865d3f43e · inbound

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise cites this paper.

$\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T05:04:04.161211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:04:04.161211Z digest=sha256:1eabeac953792d5943093b23024cfb737777ecfe5b0c54fa16fe827d7634d24f

Observation 3f19fe15-ec64-4642-96f6-c3e7acacc617 · inbound

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels cites this paper.

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective

Reference 52

Resolution
verified exact
local_arxiv, observed 2026-08-05T22:45:49.634224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T22:45:48.464077Z digest=sha256:0b0e163e9fa2400d5b97fa8c4abe42f1da0136347013d2f28e67c1528e201525