Research map / Label-efficient and robust learning
Semi-supervised learning: research map
623 accepted papers on Semi-supervised learning in Label-efficient and robust learning, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 8 approaches. The busiest year so far is 2023.
Within Label-efficient and robust learning, its share held steady from 5.5% in 2023–24 to 5.2% in 2025–26 (159 → 158 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
semi · examples · classification · 342 papers
Approaches in this cluster:
- Holistic semi-supervised pipelines (125 papers)
Combines consistency, augmentation and big self-supervised pretraining for semi-supervised classification. - Self-training theory (86 papers)
Analyzes self-training, contrastive learning and unlabeled data with theoretical guarantees. - Safe and realistic semi-supervised learning (70 papers)
Evaluates and debiases use of unlabeled samples, including moderately confident ones. - GAN-based semi-supervised learning (61 papers)
Uses generative adversarial models and perturbation regularization to exploit unlabeled data.
Most cited and most cited since 2024:
- Unsupervised Data Augmentation for Consistency Training (NeurIPS 2020 · 1,598 citations)
- Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results (NeurIPS 2017 · 1,557 citations)
- A Semi-supervised Nighttime Dehazing Baseline with Spatial-Frequency Aware and Realistic Brightness Constraint (CVPR 2024 · 74 citations)
- Systematic Comparison of Semi-supervised and Self-supervised Learning for Medical Image Classification (CVPR 2024 · 20 citations)
pseudo labels · segmentation · pseudo labeling · 281 papers
Approaches in this cluster:
- Confidence-based pseudo-label selection (134 papers)
Selects reliable pseudo-labels using uncertainty, local variance and bias-adaptive classifiers. - Consistency training for semi-supervised segmentation (49 papers)
Uses cross pseudo supervision and consistency with unreliable-label handling for segmentation. - Semi-supervised medical segmentation (50 papers)
Combines foundation models, density-aware contrast and variance analysis for medical image segmentation. - Pseudo-labeling for specialized tasks (48 papers)
Applies pseudo-label teachers to mirror detection, referring expressions, motion and rotation regression.
Most cited and most cited since 2024:
- Semi-Supervised Semantic Segmentation With Cross Pseudo Supervision (CVPR 2021 · 1,204 citations)
- Semi-Supervised Semantic Segmentation With Cross-Consistency Training (CVPR 2020 · 981 citations)
- Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation (CVPR 2024 · 110 citations)
- Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation (CVPR 2024 · 45 citations)
Related topics in Label-efficient and robust learning
- Noisy labels (462)
- Zero-/few-shot and meta-learning (784)
- Test-time adaptation (359)
- OOD and anomaly detection (614)
- Recognition and long-tail (4,953)
- Self-supervised and contrastive (988)
- Domain adaptation and generalization (1,330)
- Knowledge distillation (544)