Paper Citation Record · LEDGER

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels

As of 18 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.13342.

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

pith.paper-citation-record.v1
2505.13342 v1

Coverage vector

measured 47 of 47 reference resolution

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measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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verified fuzzy39
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 384c6d87-b39d-4fd2-a86d-dded2bbefaca · outbound

This paper cites Image clas- sification with deep learning in the presence of noisy labels: A survey.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Image clas- sification with deep learning in the presence of noisy labels: A survey

Reference 1

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Observation 4905a96c-3e41-44cf-80d4-f87ad02bc896 · outbound

This paper cites Learning from noisy examples.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Learning from noisy examples

Reference 2

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Observation 2011abd4-5540-4aed-9f2c-7c9a3a2e77d3 · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels A Simple Framework for Contrastive Learning of Visual Representations

Reference 3

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Observation 5ae7a602-63bb-464c-8d1d-fffb83f467f0 · outbound

This paper cites RandAugment: Practical automated data augmentation with a reduced search space.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels RandAugment: Practical automated data augmentation with a reduced search space

Reference 4

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This paper cites Maximum likelihood from incomplete data via the EM algorithm.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Maximum likelihood from incomplete data via the EM algorithm

Reference 5

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Observation f89f7ca7-c4a9-451f-a58f-1cfa7e322657 · outbound

This paper cites COPER: Correlation-based Permutations for Multi-View Clustering.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels COPER: Correlation-based Permutations for Multi-View Clustering

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.517699Z

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Observation 6f8f1b65-7446-4db5-b709-7d83164f1d45 · outbound

This paper cites Joint probabilistic modeling of single-cell multi-omic data with totalVI.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Joint probabilistic modeling of single-cell multi-omic data with totalVI

Reference 7

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Observation b3e1e996-b16b-476d-a6d8-6f977d4e68e5 · outbound

This paper cites Co-teaching: Ro- bust training of deep neural networks with ex- tremely noisy labels.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Co-teaching: Ro- bust training of deep neural networks with ex- tremely noisy labels

Reference 8

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This paper cites Deep self-learning from noisy labels.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Deep self-learning from noisy labels

Reference 9

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Observation 1c64c2d2-4d9b-4f32-94c5-61cdbd9902fc · outbound

This paper cites Deep residual learning for image recognition.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Deep residual learning for image recognition

Reference 10

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Observation 1bbf10cc-1faa-4b7c-98cb-f8e4e4990aa5 · outbound

This paper cites Masked Autoencoders Are Scalable Vision Learners.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Masked Autoencoders Are Scalable Vision Learners

Reference 11

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Observation cc797e38-abd3-4c8c-b745-27304df01520 · outbound

This paper cites A systematic evaluation of single-cell RNA-sequencing imputation methods.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels A systematic evaluation of single-cell RNA-sequencing imputation methods

Reference 12

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Observation d6cbb4e2-9bfc-48c3-9c57-aa393b6a6b70 · outbound

This paper cites O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.464146Z

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Observation b08d0c40-2c91-4001-a82b-6501b1d1fa99 · outbound

This paper cites MentorNet: Learning data-driven curriculum for very deep neural networks on cor- rupted labels.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels MentorNet: Learning data-driven curriculum for very deep neural networks on cor- rupted labels

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.456292Z

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Observation 8e6893f4-9521-49ea-84f8-0af117a0cefe · outbound

This paper cites Learning Multiple Layers of Features from Tiny Images.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Learning Multiple Layers of Features from Tiny Images

Reference 15

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Observation 31d2dc76-7812-4260-a4b1-31bdfe05091f · outbound

This paper cites Gradient- based learning applied to document recogni- tion.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Gradient- based learning applied to document recogni- tion

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.435693Z

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Observation b985f4df-619c-4aa8-9658-c93ec16a319f · outbound

This paper cites Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks

Reference 17

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Observation 37d6e5dd-29bf-49fc-ad81-47386731a04e · outbound

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Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Unresolved cited work

Reference 18

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This paper cites When Speaker Recognition Meets Noisy Labels: Optimizations for Front-Ends and Back- Ends.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels When Speaker Recognition Meets Noisy Labels: Optimizations for Front-Ends and Back- Ends

Reference 19

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Observation b23c3c27-0b1f-43c2-82f6-dceefe585b74 · outbound

This paper cites In: Advances in Neural Information Processing Systems (NeurIPS).

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels In: Advances in Neural Information Processing Systems (NeurIPS)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.395944Z

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Observation a48d7f77-da97-40c2-bfd1-8edaaaaab6ee · outbound

This paper cites Provably End-to-end Label- Noise Learning without Anchor Points.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Provably End-to-end Label- Noise Learning without Anchor Points

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.384986Z

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Observation ead57efc-0186-4942-9fac-71eee5dad34b · outbound

This paper cites Transductive and inductive outlier de- tection with robust autoencoders.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Transductive and inductive outlier de- tection with robust autoencoders

Reference 22

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Observation 7359d0bc-e30a-4238-8733-4bc5daeb27c2 · outbound

This paper cites Learning coupled embedding using multiview diffusion maps.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Learning coupled embedding using multiview diffusion maps

Reference 23

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Observation 5555a5e5-7889-45b1-8b9e-525331325d37 · outbound

This paper cites Multi-channel fusion for seismic event detection and classification.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Multi-channel fusion for seismic event detection and classification

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.354157Z

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Observation 1c9771bf-e355-420e-b54d-d4fca41e5adc · outbound

This paper cites Iden- tifiability of Label Noise Transition Matrix.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Iden- tifiability of Label Noise Transition Matrix

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.344963Z

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Observation 537d8d5e-931f-4b8f-9d22-fd7ee5cdd557 · outbound

This paper cites Decou- pling.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Decou- pling

Reference 26

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Observation dcf96049-2f36-44e8-895f-da66c304cbb6 · outbound

This paper cites Integrated analysis of multimodal single-cell data.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Integrated analysis of multimodal single-cell data

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.325118Z

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Observation dbc0cd6a-0e8f-409a-bcc3-3e748a1771fe · outbound

This paper cites Finite mixture modeling with mixture outcomes using the EM algorithm.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Finite mixture modeling with mixture outcomes using the EM algorithm

Reference 28

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Observation 5df9a43c-6c55-4343-895d-d983e1006baa · outbound

This paper cites Opti- mized threshold inference for partitioning of clones from high-throughput B cell repertoire sequencing data.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Opti- mized threshold inference for partitioning of clones from high-throughput B cell repertoire sequencing data

Reference 29

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Observation 7e78d25c-6208-4728-abdc-0464e95efe6c · outbound

This paper cites Making deep neural net- works robust to label noise: A loss correction approach.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Making deep neural net- works robust to label noise: A loss correction approach

Reference 30

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Observation c1cae3d6-71b8-4cf3-ac33-1c294d1a8b7c · outbound

This paper cites Contaminated speech training methods for robust DNN-HMM distant speech recognition.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Contaminated speech training methods for robust DNN-HMM distant speech recognition

Reference 31

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Observation 4ef113af-514d-4109-926a-26a15d39cc0e · outbound

This paper cites Gaussian mix- ture models.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Gaussian mix- ture models

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.287139Z

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Observation 4e063443-6211-4803-a78c-3cf09fb77334 · outbound

This paper cites Anomaly Detection with Variance Stabilized Density Estimation.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Anomaly Detection with Variance Stabilized Density Estimation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.276375Z

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This paper cites Domain-Generalizable Multiple-Domain Clustering.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Domain-Generalizable Multiple-Domain Clustering

Reference 34

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Observation e3805746-ae20-4942-87eb-2a4dbff577bb · outbound

This paper cites Cyclical learning rates for training neural networks.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Cyclical learning rates for training neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.256333Z

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Observation 91d647f9-7f45-4aab-b54f-cda949e45aec · outbound

This paper cites Learning from noisy labels with deep neural networks: A survey.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Learning from noisy labels with deep neural networks: A survey

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.247007Z

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Observation 9d4af767-63b2-4961-ba86-6965dac5b7b9 · outbound

This paper cites Symmetric cross entropy for robust learning with noisy labels.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Symmetric cross entropy for robust learning with noisy labels

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.236528Z

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

This paper cites Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations.

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

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This paper cites Learning to teach with dynamic loss functions.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Learning to teach with dynamic loss functions

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.225909Z

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Observation b4a66d24-1203-4ea5-a496-388a6023beac · outbound

This paper cites NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.214767Z

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Observation 39a0bf96-b70d-4021-b275-779d185947e5 · outbound

This paper cites Are Anchor Points Really Indispensable in Label-Noise Learning? 2019.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Are Anchor Points Really Indispensable in Label-Noise Learning? 2019

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.204743Z

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Observation a0365dfb-19f2-4fd1-982a-a01e98313ecb · outbound

This paper cites L_dmi: A novel information- theoretic loss function for training deep nets ro- bust to label noise.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels L_dmi: A novel information- theoretic loss function for training deep nets ro- bust to label noise

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.194317Z

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Observation 8023a7cb-6d5c-4328-8722-bbf877d15e90 · outbound

This paper cites Dual t: Reducing estimation error for transition matrix in label-noise learning.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Dual t: Reducing estimation error for transition matrix in label-noise learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.183378Z

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Observation a9c02466-af0a-4851-82bd-7cbebd9bb5dd · outbound

This paper cites Probabilistic end-to- end noise correction for learning with noisy la- bels.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Probabilistic end-to- end noise correction for learning with noisy la- bels

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.172470Z

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Observation e80f0cef-6499-430c-be9e-a3d1311daafb · outbound

This paper cites Challenges and emerg- ing directions in single-cell analysis.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels Challenges and emerg- ing directions in single-cell analysis

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.162755Z

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Observation d011f249-9404-43b0-9454-127b51c270b6 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels mixup: Beyond Empirical Risk Minimization

Reference 46

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Observation 54d0747d-a870-4473-84a2-d251b7a80dae · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels A Simple Framework for Contrastive Learning of Visual Representations

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T20:18:57.127090Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.