DivideMix: Learning with Noisy Labels as Semi-supervised Learning | Paper Citation Record

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

Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 55

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Training a Label-Noise-Resistant GNN with Reduced Complexity cites this paper.

Training a Label-Noise-Resistant GNN with Reduced Complexity DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 21

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

Dataset Distillers Are Good Label Denoisers In the Wild DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 24

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Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise cites this paper.

Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 36

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Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Grounding cites this paper.

Paint Outside the Box: Synthesizing and Selecting Training Data for Visual Grounding DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 36

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ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization cites this paper.

ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 25

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Suppressing Uncertainty in Gaze Estimation cites this paper.

Suppressing Uncertainty in Gaze Estimation DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 17

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

Learning Causal Transition Matrix for Instance-dependent Label Noise DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 17

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ProtCLIP: Function-Informed Protein Multi-Modal Learning cites this paper.

ProtCLIP: Function-Informed Protein Multi-Modal Learning DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 26

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An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise cites this paper.

An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 49

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Open set label noise learning with robust sample selection and margin-guided module cites this paper.

Open set label noise learning with robust sample selection and margin-guided module DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 9

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Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection cites this paper.

Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 16

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Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction cites this paper.

Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 12

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Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback cites this paper.

Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 50

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Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing cites this paper.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 24

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Why Can Accurate Models Be Learned from Inaccurate Annotations? cites this paper.

Why Can Accurate Models Be Learned from Inaccurate Annotations? DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 25

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Ambiguity-Restrained Text-Video Representation Learning for Partially Relevant Video Retrieval cites this paper.

Ambiguity-Restrained Text-Video Representation Learning for Partially Relevant Video Retrieval DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 22

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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 DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 33

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Commuting Distance Regularization for Timescale-Dependent Label Inconsistency in EEG Emotion Recognition cites this paper.

Commuting Distance Regularization for Timescale-Dependent Label Inconsistency in EEG Emotion Recognition DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 23

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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 DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 14

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Differential-informed Sample Selection Accelerates Multimodal Contrastive Learning cites this paper.

Differential-informed Sample Selection Accelerates Multimodal Contrastive Learning DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 26

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Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning cites this paper.

Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 9

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TRUST: Leveraging Text Robustness for Unsupervised Domain Adaptation cites this paper.

TRUST: Leveraging Text Robustness for Unsupervised Domain Adaptation DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 31

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Count2Density: Crowd Density Estimation without Location-level Annotations cites this paper.

Count2Density: Crowd Density Estimation without Location-level Annotations DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 28

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Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition cites this paper.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 34

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LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training cites this paper.

LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 14

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Towards Continual Expansion of Data Coverage: Automatic Text-guided Edge-case Synthesis cites this paper.

Towards Continual Expansion of Data Coverage: Automatic Text-guided Edge-case Synthesis DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 18

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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 DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 23

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Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction cites this paper.

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 2019

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NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning cites this paper.

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 21

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Reliable Mislabel Detection for Video Capsule Endoscopy Data cites this paper.

Reliable Mislabel Detection for Video Capsule Endoscopy Data DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 13

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Can LLMs Learn to Reason Robustly under Noisy Supervision? cites this paper.

Can LLMs Learn to Reason Robustly under Noisy Supervision? DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 12

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Observation 89cd6b54-064e-4e63-afac-31c2d3c61121 · inbound

Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise cites this paper.

Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 34

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Boxes2Pixels: Learning Defect Segmentation from Noisy SAM Masks cites this paper.

Boxes2Pixels: Learning Defect Segmentation from Noisy SAM Masks DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 14

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Observation 3f3889da-3893-4da0-b866-47928f5d44c1 · inbound

Analyzing the Effect of Noise in LLM Fine-tuning cites this paper.

Analyzing the Effect of Noise in LLM Fine-tuning DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 11

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Detecting and refurbishing ground truth errors during training of deep learning-based echocardiography segmentation models cites this paper.

Detecting and refurbishing ground truth errors during training of deep learning-based echocardiography segmentation models DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 13

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See Through the Noise: Improving Domain Generalization in Gaze Estimation cites this paper.

See Through the Noise: Improving Domain Generalization in Gaze Estimation DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 19

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Observation ef83c46e-2bbe-48d1-8503-7a065ab5f149 · inbound

Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization cites this paper.

Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 6

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Model-agnostic information transfer and fusion for classification with label noise cites this paper.

Model-agnostic information transfer and fusion for classification with label noise DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 3

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Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise cites this paper.

Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 47

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SEI-SHIELD: Robust Specific Emitter Identification Under Label Noise Via Self-Supervised Filtering and Iterative Rescue cites this paper.

SEI-SHIELD: Robust Specific Emitter Identification Under Label Noise Via Self-Supervised Filtering and Iterative Rescue DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 34

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Observation 5de97c36-d61d-4f35-bef4-86223afe342c · inbound

HamBR: Active Decision Boundary Restoration Based on Hamiltonian Dynamics for Learning with Noisy Labels cites this paper.

HamBR: Active Decision Boundary Restoration Based on Hamiltonian Dynamics for Learning with Noisy Labels DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 21

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Observation 1229dc5c-0e87-4cd1-8084-2c89e5346cdc · inbound

Efficient Bilevel Optimization for Meta Label Correction in Noisy Label Learning cites this paper.

Efficient Bilevel Optimization for Meta Label Correction in Noisy Label Learning DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:48:17.486799Z

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Observation 67dea327-3fe9-4b7c-91be-0e7ba4beb564 · inbound

Holistic Reliability Propagation: Decoupling Annotation and Prediction for Robust Noisy-Label cites this paper.

Holistic Reliability Propagation: Decoupling Annotation and Prediction for Robust Noisy-Label DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:23:58.326901Z

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Observation 9bc3f2ea-8c9f-45a7-8af0-134d61a6d17c · inbound

GAMR: Geometric-Aware Manifold Regularization with Virtual Outlier Synthesis for Learning with Noisy Labels cites this paper.

GAMR: Geometric-Aware Manifold Regularization with Virtual Outlier Synthesis for Learning with Noisy Labels DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:19:39.228796Z

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Observation 1ac28560-0add-466e-b68d-3e78d05ef055 · inbound

Robust Fuzzy Multi-view Learning under View Conflict cites this paper.

Robust Fuzzy Multi-view Learning under View Conflict DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-06-30T14:24:45.288050Z

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Observation 64d5dfbf-8245-40c3-bff2-b6c48fe73931 · inbound

ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search cites this paper.

ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 287

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Observation 47ec4d80-5130-4d7a-a62f-8d543446c8c2 · inbound

ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search cites this paper.

ROGLE: Robust Global-Local Alignment with Automated Region Supervision for Text-Based Person Search DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 287

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:17:28.870097Z

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Observation 8e85b931-4d38-4177-94e1-82fd1e8f899f · inbound

Organizational Control Layer: Governance Infrastructure at the Execution Boundary of LLM Agent Systems cites this paper.

Organizational Control Layer: Governance Infrastructure at the Execution Boundary of LLM Agent Systems DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 265

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Observation 249f34c5-e992-4642-9695-3a12bc5c1d3c · inbound

Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching cites this paper.

Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-03T09:07:47.671872Z

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Observation 3dd02760-0c9d-4e46-98f9-e7d8283335ed · inbound

Noise-Aware Framework for Correcting Corrupted Labels cites this paper.

Noise-Aware Framework for Correcting Corrupted Labels DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-03T09:07:47.585627Z

Source-reported events for the cited work

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Observation 923962cb-cdbf-4346-966d-27059c7803aa · inbound

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

Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T07:14:22.011482Z

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Virtual Category-Guided Continual Generalized Category Discovery cites this paper.

Virtual Category-Guided Continual Generalized Category Discovery DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 39

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Weak-to-Strong Generalization via Direct On-Policy Distillation cites this paper.

Weak-to-Strong Generalization via Direct On-Policy Distillation DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 78

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Weak-to-Strong Generalization via Direct On-Policy Distillation cites this paper.

Weak-to-Strong Generalization via Direct On-Policy Distillation DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 74

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LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling cites this paper.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 13

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Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation cites this paper.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 29

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