Data to 14 Oct 2026

Learning to Learn from Noisy Labeled Data

arXiv 1812.05214 ↗First posted 13 Dec 2018cs.LG cs.CV stat.MLcited by 160

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Abstract (arXiv)

Despite the success of deep neural networks (DNNs) in image classification tasks, the human-level performance relies on massive training data with high-quality manual annotations, which are expensive and time-consuming to collect. There exist many inexpensive data sources on the web, but they tend to contain inaccurate labels. Training on noisy labeled datasets causes performance degradation because DNNs can easily overfit to the label noise. To overcome this problem, we propose a noise-tolerant training algorithm, where a meta-learning update is performed prior to conventional gradient update. The proposed meta-learning method simulates actual training by generating synthetic noisy labels, and train the model such that after one gradient update using each set of synthetic noisy labels, the model does not overfit to the specific noise. We conduct extensive experiments on the noisy CIFAR-10 dataset and the Clothing1M dataset. The results demonstrate the advantageous performance of the proposed method compared to several state-of-the-art baselines.

Key facts. Cited by 160 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Dec 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 60; 2022: 41; 2023: 26; 2024: 14; 2025: 11; 2026: 8.

160 papers.

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2021Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentationcited by 268
2021Learning with Feature-Dependent Label Noise: A Progressive Approachcited by 102
2022Robust Training under Label Noise by Over-parameterizationcited by 93
2021Augmentation Strategies for Learning with Noisy Labelscited by 86
2021Breaking the Dilemma of Medical Image-to-image Translationcited by 82
2021Jo-SRC: A Contrastive Approach for Combating Noisy Labelscited by 80
2021Robust Audio-Visual Instance Discriminationcited by 80
2022UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learningcited by 77
2021Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labelscited by 74
2021Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labelscited by 69
2021Learning Fast Sample Re-weighting Without Reward Datacited by 69
2021Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularizationcited by 62

38 more

2021NGC: A Unified Framework for Learning with Open-World Noisy Datacited by 58
2022Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmarkcited by 56
2022Learning with Neighbor Consistency for Noisy Labelscited by 55
2022Robust Cross-Modal Representation Learning with Progressive Self-Distillationcited by 55
2021Co-learning: Learning from Noisy Labels with Self-supervisioncited by 51
2021Hard Sample Aware Noise Robust Learning for Histopathology Image Classificationcited by 48
2022Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clusteringcited by 44
2021LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environmentcited by 40
2021Faster Meta Update Strategy for Noise-Robust Deep Learningcited by 38
2021Contrastive Learning Improves Model Robustness Under Label Noisecited by 36
2021Joint Negative and Positive Learning for Noisy Labelscited by 34
2021Boosting Co-teaching with Compression Regularization for Label Noisecited by 31
2022Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasetscited by 28
2021Continual Learning on Noisy Data Streams via Self-Purified Replaycited by 27
2022Sparse Double Descent: Where Network Pruning Aggravates Overfittingcited by 26
2023Noisy Correspondence Learning with Meta Similarity Correctioncited by 26
2021Visual Distant Supervision for Scene Graph Generationcited by 25
2021Noise-resistant Deep Metric Learning with Ranking-based Instance Selectioncited by 23
2022Few-shot Learning with Noisy Labelscited by 23
2022Neighborhood Collective Estimation for Noisy Label Identification and Correctioncited by 22
2021Adaptive Sample Selection for Robust Learning under Label Noisecited by 21
2022When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Methodcited by 21
2023Learning from Noisy Labels with Decoupled Meta Label Purifiercited by 21
2023Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?cited by 21
2021Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrectioncited by 20
2023Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labelscited by 20
2021Memorization and Generalization in Neural Code Intelligence Modelscited by 19
2021A Tale Of Two Long Tailscited by 19
2022Informative Pseudo-Labeling for Graph Neural Networks with Few Labelscited by 19
2023Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Trainingcited by 19
2021Consistency Regularization Can Improve Robustness to Label Noisecited by 18
2021Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noisecited by 18
2021PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labelscited by 17
2022On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classificationcited by 17
2023CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labelscited by 17
2021Learning from Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentationcited by 16
2021Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Datacited by 16
2023AQuA: A Benchmarking Tool for Label Quality Assessmentcited by 16

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