Research map / Label-efficient and robust learning / Semi-supervised learning
Robust Semi-Supervised Learning through Label Aggregation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace majority-vote bagging with weighted label aggregation (MLKL) using unlabeled data | ensemble of 50 linear SVMs · rcv1-train | accuracy (%): 73.17 -> 78.77 | Helped |
| ROSSEL instead of ensemble-SVM | linear SVM ensemble · connect4-10k | accuracy (%): 65.70 -> 68.20 | Helped |
| ROSSEL instead of ensemble-SVM | linear SVM ensemble · dna | accuracy (%): 84.63 -> 88.52 | Helped |
| ROSSEL instead of ASL (graph-free SSL baseline) | dna · dna | accuracy (%): 90.03 -> 88.52 | Hurt |
| increase weak annotators from 10 to 50 in ROSSEL (ablation) | ROSSEL · rcv1-train | accuracy (%): 79.22 -> 78.77 | No clear effect |
| increase weak annotators from 10 to 50 in ROSSEL (ablation) | ROSSEL · rcv1-train | training time (s): 45.5584 -> 336.4487 | Hurt |
| increase number of weak annotators in ensemble-SVM (ablation) | ensemble-SVM · CNAE9 | accuracy (%): 79.75 -> 81.56 | Helped |
| ROSSEL under label noise (2% to 10% flipped labels) vs other SSL methods | ROSSEL · CNAE9, dna | accuracy (%) | Helped |
Closest papers by meaning
- Learning From Semi-Supervised Weak-Label Data (AAAI 2018 · similarity 0.89)
- Keep It on a Leash: Controllable Pseudo-label Generation Towards Realistic Long-Tailed Semi-Supervised Learning (NeurIPS 2025 · similarity 0.88)
- Semi-supervised Multi-label Learning with Balanced Binary Angular Margin Loss (NeurIPS 2024 · similarity 0.88)
- Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data (AAAI 2024 · similarity 0.87)
- Correlation-Induced Label Prior for Semi-Supervised Multi-Label Learning (ICML 2024 · similarity 0.86)
- Bi-Level Optimization for Semi-Supervised Learning with Pseudo-Labeling (AAAI 2025 · similarity 0.86)
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