Paper Citation Record · LEDGER

Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2110.12088.

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

pith.paper-citation-record.v1
2110.12088 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:14:56.171023Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T04:39:34.343416Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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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 b83c2a39-2f44-4537-9355-f50620b47f91 · inbound

Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond cites this paper.

Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-23T21:48:28.686366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 63f663e8-69e1-4caf-a1c0-8ce109b813bf · inbound

On the Importance of Embedding Norms in Self-Supervised Learning cites this paper.

On the Importance of Embedding Norms in Self-Supervised Learning Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T22:14:56.171023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 178d75ca-598e-461e-955e-27494888a8d1 · inbound

Laplace Sample Information: Data Informativeness Through a Bayesian Lens cites this paper.

Laplace Sample Information: Data Informativeness Through a Bayesian Lens Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:09.350785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fff03e6f-efb9-46e4-9917-c63d21c172e5 · inbound

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification cites this paper.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:11.464051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e75efdb-6c03-46a3-8bb6-ca32d5cef319 · inbound

Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement cites this paper.

Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T14:14:24.067123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaf85289-2014-4ece-85f3-d75b52091242 · inbound

Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark cites this paper.

Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:54.851445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25a63add-d19f-4802-b21a-0ac962f49bfe · inbound

CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels cites this paper.

CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T17:06:42.921452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fd587d9-4a72-46ff-a99d-ffee3889018d · inbound

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning cites this paper.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T16:45:03.656920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96d728ec-6ded-409e-90c8-4e6279bdff36 · inbound

Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning cites this paper.

Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 164

Resolution: unresolved; no resolver link, observed 2026-08-05T22:09:11.279555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d84f7459-682f-4997-9796-585d94b5aeef · inbound

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook cites this paper.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:58:42.729209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a2e41adb-b417-4f32-ae0d-85360d5ea8db · inbound

CAMEO: A Conditional and Quality-Aware Multi-Agent Image Editing Orchestrator cites this paper.

CAMEO: A Conditional and Quality-Aware Multi-Agent Image Editing Orchestrator Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T21:03:20.303046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e5038589-7117-4b9a-aa35-63922b516e2a · inbound

Do Not Imitate, Reinforce: Iterative Classification via Belief Refinement cites this paper.

Do Not Imitate, Reinforce: Iterative Classification via Belief Refinement Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T14:26:04.350403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fbf96cb6-c5a3-4dbf-823d-2f2de2433e16 · inbound

Medical Model Synthesis Architectures: A Case Study cites this paper.

Medical Model Synthesis Architectures: A Case Study Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 242

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:21:24.804879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 784a4f36-4d62-4853-9c01-feec35bd746f · inbound

SafeLens: Deliberate and Efficient Video Guardrails with Fast-and-Slow Screening cites this paper.

SafeLens: Deliberate and Efficient Video Guardrails with Fast-and-Slow Screening Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:08:21.155003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 38f621ef-cb02-451f-ad5d-3926b0c5fb80 · inbound

Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification cites this paper.

Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:07:56.226068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7ec296b1-bdb0-45d1-81c3-5c44364b60ae · inbound

Tri-Info: Generalizable, Interpretable Failure Prediction for VLA Models via Information Theory cites this paper.

Tri-Info: Generalizable, Interpretable Failure Prediction for VLA Models via Information Theory Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 182

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T04:39:34.344901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 92388726-c826-4ebd-8b63-41d0d756c321 · inbound

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets cites this paper.

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:14:22.051694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.