semi-supervised learning
A machine learning technique that combines a small amount of labeled data with a large amount of unlabeled data to improve model learning effectiveness.
- Adversarial Graph Fusion for Incomplete Multi-view Semi-supervised Learning with Tensorial Imputation
- Fourier Clouds: Fast Bias Correction for Imbalanced Semi-Supervised Learning
- Functional Virtual Adversarial Training for Semi-Supervised Time Series Classification
- Geometric Imbalance in Semi-Supervised Node Classification
- Keep It on a Leash: Controllable Pseudo-label Generation Towards Realistic Long-Tailed Semi-Supervised Learning
- L2DGCN: Learnable Enhancement and Label Selection Dynamic Graph Convolutional Networks for Mitigating Degree Bias
- Memorization in Graph Neural Networks
- On the sample complexity of semi-supervised multi-objective learning
- Prediction-Powered Semi-Supervised Learning with Online Power Tuning
- RankMatch: A Novel Approach to Semi-Supervised Label Distribution Learning Leveraging Rank Correlation between Labels
- Revisiting Semi-Supervised Learning in the Era of Foundation Models
- Roboflow100-VL: A Multi-Domain Object Detection Benchmark for Vision-Language Models
- Segment Anything Model Meets Semi-supervised Medical Image Segmentation: A Novel Perspective
- Semi-Supervised Regression with Heteroscedastic Pseudo-Labels
- Semi-supervised Graph Anomaly Detection via Robust Homophily Learning
- Semi-supervised Vertex Hunting, with Applications in Network and Text Analysis
- TRiCo: Triadic Game-Theoretic Co-Training for Robust Semi-Supervised Learning
- Theoretical Insights into In-context Learning with Unlabeled Data
- Towards Robust Pseudo-Label Learning in Semantic Segmentation: An Encoding Perspective
- Uncertain Knowledge Graph Completion via Semi-Supervised Confidence Distribution Learning
- Uncertainty-Informed Meta Pseudo Labeling for Surrogate Modeling with Limited Labeled Data