Data to 14 Oct 2026

Probabilistic End-to-end Noise Correction for Learning with Noisy Labels

arXiv 1903.07788 ↗First posted 19 Mar 2019cs.CV cs.LGcited by 210

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Abstract (arXiv)

Deep learning has achieved excellent performance in various computer vision tasks, but requires a lot of training examples with clean labels. It is easy to collect a dataset with noisy labels, but such noise makes networks overfit seriously and accuracies drop dramatically. To address this problem, we propose an end-to-end framework called PENCIL, which can update both network parameters and label estimations as label distributions. PENCIL is independent of the backbone network structure and does not need an auxiliary clean dataset or prior information about noise, thus it is more general and robust than existing methods and is easy to apply. PENCIL outperforms previous state-of-the-art methods by large margins on both synthetic and real-world datasets with different noise types and noise rates. Experiments show that PENCIL is robust on clean datasets, too.

Key facts. Cited by 210 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Mar 2019.

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Citing papers per year: 2021: 66; 2022: 52; 2023: 30; 2024: 33; 2025: 21; 2026: 8.

210 papers.

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2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 467
2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
2022Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learntcited by 236
2022Selective-Supervised Contrastive Learning with Noisy Labelscited by 112
2021Learning with Feature-Dependent Label Noise: A Progressive Approachcited by 102
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
2021Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labelingcited by 84
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
2021Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labelscited by 69

38 more

2021Learning Fast Sample Re-weighting Without Reward Datacited by 69
2022Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognitioncited by 67
2021Adaptive Early-Learning Correction for Segmentation from Noisy Annotationscited by 62
2021NGC: A Unified Framework for Learning with Open-World Noisy Datacited by 58
2021Hard Sample Aware Noise Robust Learning for Histopathology Image Classificationcited by 48
2022ProMix: Combating Label Noise via Maximizing Clean Sample Utilitycited by 48
2021FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentationcited by 45
2023Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Taskscited by 41
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
2022Large Loss Matters in Weakly Supervised Multi-Label Classificationcited by 38
2021Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterioncited by 36
2021Joint Negative and Positive Learning for Noisy Labelscited by 34
2021Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approachcited by 32
2021Boosting Co-teaching with Compression Regularization for Label Noisecited by 31
2022Uncertainty-aware Label Distribution Learning for Facial Expression Recognitioncited by 30
2022Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsingcited by 29
2022How to Reduce Change Detection to Semantic Segmentationcited by 28
2022Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasetscited by 28
2023Twin Contrastive Learning with Noisy Labelscited by 28
2021Continual Learning on Noisy Data Streams via Self-Purified Replaycited by 27
2022Towards Federated Learning against Noisy Labels via Local Self-Regularizationcited by 26
2022Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamicscited by 26
2022SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labelscited by 24
2022From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Modelcited by 22
2022Neighborhood Collective Estimation for Noisy Label Identification and Correctioncited by 22
2023Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learningcited by 22
2021Adaptive Sample Selection for Robust Learning under Label Noisecited by 21
2022On Learning Contrastive Representations for Learning with Noisy Labelscited by 21
2022Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformercited 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
2021A Tale Of Two Long Tailscited by 19
2023SILT: Shadow-aware Iterative Label Tuning for Learning to Detect Shadows from Noisy Labelscited by 19
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

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