Research map / Label-efficient and robust learning
Noisy labels: research map
462 accepted papers on Noisy labels in Label-efficient and robust learning, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 7 approaches. The busiest year so far is 2026.
Within Label-efficient and robust learning, its share held steady from 4.2% in 2023–24 to 4.0% in 2025–26 (121 → 122 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
noisy labels · robust · clean · 256 papers
Approaches in this cluster:
- Sample selection and co-training (86 papers)
Separates clean from noisy samples using small-loss selection, co-teaching and joint optimization. - Noise-aware loss and pairwise methods (66 papers)
Reduces noise effects through pairwise similarity, open-set noise and noise detection. - Contrastive learning on noisy data (55 papers)
Applies contrastive and diffusion-based training to noisy, imbalanced or weakly labeled recognition. - Noisy cross-modal hashing and multi-label (49 papers)
Handles noisy labels in cross-modal retrieval and multi-label classification via robust hashing and loss handling.
Most cited and most cited since 2024:
- Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels (NeurIPS 2018 · 1,467 citations)
- Co-teaching: Robust training of deep neural networks with extremely noisy labels (NeurIPS 2018 · 1,182 citations)
- Learning with Structural Labels for Learning with Noisy Labels (CVPR 2024 · 20 citations)
- L2B: Learning to Bootstrap Robust Models for Combating Label Noise (CVPR 2024 · 20 citations)
label noise · transition · instance · 206 papers
Approaches in this cluster:
- Instance-dependent noise modeling (106 papers)
Estimates feature-dependent noise transitions and correction for instance-level label noise. - Loss correction and noise robustness theory (77 papers)
Corrects losses and analyzes how overfitting and subclass structure affect noisy training. - Crowdsourced label aggregation (23 papers)
Infers true labels from noisy annotators using worker confusion and ability models.
Most cited and most cited since 2024:
- Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach (CVPR 2017 · 1,463 citations)
- Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection (CVPR 2019 · 594 citations)
- Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning (AAAI 2024 · 32 citations)
- Dual Self-Paced Cross-Modal Hashing (AAAI 2024 · 28 citations)