Data to 7 Oct 2026

MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels

arXiv 1712.05055 ↗First posted 14 Dec 2017cs.CVcited by 652

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Abstract (arXiv)

Recent deep networks are capable of memorizing the entire data even when the labels are completely random. To overcome the overfitting on corrupted labels, we propose a novel technique of learning another neural network, called MentorNet, to supervise the training of the base deep networks, namely, StudentNet. During training, MentorNet provides a curriculum (sample weighting scheme) for StudentNet to focus on the sample the label of which is probably correct. Unlike the existing curriculum that is usually predefined by human experts, MentorNet learns a data-driven curriculum dynamically with StudentNet. Experimental results demonstrate that our approach can significantly improve the generalization performance of deep networks trained on corrupted training data. Notably, to the best of our knowledge, we achieve the best-published result on WebVision, a large benchmark containing 2.2 million images of real-world noisy labels. The code are at https://github.com/google/mentornet

Key facts. Cited by 652 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Dec 2017.

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Citing papers per year: 2020: 1; 2021: 174; 2022: 141; 2023: 127; 2024: 94; 2025: 62; 2026: 53.

652 papers.

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2021Curriculum Learning: A Surveycited by 257
2021Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotationscited by 253
2024WARM: On the Benefits of Weight Averaged Reward Modelscited by 145
2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
2022Selective-Supervised Contrastive Learning with Noisy Labelscited by 112
2022Curriculum Temperature for Knowledge Distillationcited by 110
2021Regularizing Generative Adversarial Networks under Limited Datacited by 105
2021Learning with Feature-Dependent Label Noise: A Progressive Approachcited by 102
2021Provably End-to-end Label-Noise Learning without Anchor Pointscited by 97
2021Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognitioncited by 89
2021Augmentation Strategies for Learning with Noisy Labelscited by 86
2021FINE Samples for Learning with Noisy Labelscited by 82

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2021Jo-SRC: A Contrastive Approach for Combating Noisy Labelscited by 80
2021Sample Selection with Uncertainty of Losses for Learning with Noisy Labelscited by 79
2022UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learningcited by 77
2021Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labelscited by 74
2021NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphscited by 73
2021To Smooth or Not? When Label Smoothing Meets Noisy Labelscited by 70
2021Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labelscited by 69
2021Learning Fast Sample Re-weighting Without Reward Datacited by 69
2022Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognitioncited by 67
2021Clusterability as an Alternative to Anchor Points When Learning with Noisy Labelscited by 65
2023Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptationcited by 63
2021Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularizationcited by 62
2022The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generationcited by 60
2021Open-set Label Noise Can Improve Robustness Against Inherent Label Noisecited by 58
2021NGC: A Unified Framework for Learning with Open-World Noisy Datacited by 58
2021Detecting Corrupted Labels Without Training a Model to Predictcited by 57
2021Exploring Memorization in Adversarial Trainingcited by 55
2022DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotationscited by 54
2023Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runscited by 52
2021Co-learning: Learning from Noisy Labels with Self-supervisioncited by 50
2021Instance-dependent Label-noise Learning under a Structural Causal Modelcited by 50
2022Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learningcited by 49
2021From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimationcited by 48
2021Sample Selection for Fair and Robust Trainingcited by 48
2021Hard Sample Aware Noise Robust Learning for Histopathology Image Classificationcited by 48
2022ProMix: Combating Label Noise via Maximizing Clean Sample Utilitycited by 48
2021Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptationcited by 47
2021Asymmetric Loss Functions for Learning with Noisy Labelscited by 46
2022Investigating Why Contrastive Learning Benefits Robustness Against Label Noisecited by 46
2022Robust Contrastive Learning against Noisy Viewscited by 45
2021Active label cleaning for improved dataset quality under resource constraintscited by 44
2021MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentationcited by 43
2021Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptationcited by 42
2021On Tilted Losses in Machine Learning: Theory and Applicationscited by 42
2022EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbonescited by 42
2021Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentationcited by 41
2021LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environmentcited by 40
2022Mitigating Memorization of Noisy Labels by Clipping the Model Predictioncited by 40

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