Paper Citation Record · LEDGER

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

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 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 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 29 of 29 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:18:40.317605Z

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

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External citation measurements

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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 2026-08-18T06:34:40.430872+00:00.

Observation 5c5ad40c-fbb7-48e5-971a-09e8e61b3ba0 · inbound

Dataset Distillers Are Good Label Denoisers In the Wild cites this paper.

Dataset Distillers Are Good Label Denoisers In the Wild Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T18:44:00.171096Z

Source-reported events for the cited work

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Observation eb24d644-ce9d-4c7d-aa0c-bff5d8764cef · inbound

Selfish Evolution: Making Discoveries in Extreme Label Noise with the Help of Overfitting Dynamics cites this paper.

Selfish Evolution: Making Discoveries in Extreme Label Noise with the Help of Overfitting Dynamics Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T11:56:54.486131Z

Source-reported events for the cited work

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Observation 8a921c3f-9bd0-43b4-8838-317c1fd35399 · inbound

Learning Causal Transition Matrix for Instance-dependent Label Noise cites this paper.

Learning Causal Transition Matrix for Instance-dependent Label Noise Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:28.173285Z

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Observation c7420755-945f-44a3-905a-c5cbbaf8d0af · inbound

Effects of Robot Competency and Motion Legibility on Human Correction Feedback cites this paper.

Effects of Robot Competency and Motion Legibility on Human Correction Feedback Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:43.142361Z

Source-reported events for the cited work

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Observation 253604fd-a181-45ac-9237-78a84855edd6 · inbound

Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels cites this paper.

Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T17:45:46.048367Z

Source-reported events for the cited work

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Observation e6042159-613c-46cc-9815-28b4faad2517 · inbound

Early Stopping Against Label Noise Without Validation Data cites this paper.

Early Stopping Against Label Noise Without Validation Data Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T12:26:42.656031Z

Source-reported events for the cited work

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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

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Observation 793f0b0b-2aae-441e-a55c-c64bb7e2653a · inbound

Inducing Robustness in a 2 Dimensional Direct Preference Optimization Paradigm cites this paper.

Inducing Robustness in a 2 Dimensional Direct Preference Optimization Paradigm Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T04:18:40.317605Z

Source-reported events for the cited work

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Observation a727cc2b-4dbc-42e7-99b8-52dd1923731a · inbound

Modular Federated Learning: A Meta-Framework Perspective cites this paper.

Modular Federated Learning: A Meta-Framework Perspective Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 284

Resolution: unresolved; no resolver link, observed 2026-08-15T21:53:23.581163Z

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Observation fe053807-2f55-40cf-93d0-37b6e17f65a1 · inbound

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning cites this paper.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T20:48:52.315327Z

Source-reported events for the cited work

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Observation 78e11f5c-d982-4dab-a9ab-29e7344220b2 · inbound

GUARD: Generation-time LLM Unlearning via Adaptive Restriction and Detection cites this paper.

GUARD: Generation-time LLM Unlearning via Adaptive Restriction and Detection Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-15T20:21:39.922316Z

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Observation aae85c75-3f0c-4875-a881-2f91a5a06488 · inbound

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels cites this paper.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T20:18:57.922171Z

Source-reported events for the cited work

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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

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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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+00:00.

Observation 013785d3-129f-4b7e-a202-b6abcb644ee4 · inbound

Confidence Calibration of Deep Learning Systems cites this paper.

Confidence Calibration of Deep Learning Systems Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-16T00:21:57.313395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.