Research map / Label-efficient and robust learning / Semi-supervised learning
Semi-Supervised Streaming Learning with Emerging New Labels
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use SEEN instead of best competing method | semi-supervised streaming classification with emerging classes · segment, satimage, usps, pendigits | accuracy: average +0.023 accuracy | Helped |
| use SEEN instead of best competing method | semi-supervised streaming classification with emerging classes · segment, satimage, usps, pendigits | macro F1: average +0.044 F1 | Helped |
| use SEEN instead of SENC-Mas | usps stream · usps | F1: 0.785 -> 0.784 | No clear effect |
| add SEENForest detector with unlabeled data instead of iForest+SVM | SEEN · four streams | accuracy: segment 0.520 -> 0.760 | Helped |
| increase number of labeled instances per class (ablation) | SEEN · segment | accuracy | Helped |
Closest papers by meaning
- Semi-Supervised Learning on Data Streams via Temporal Label Propagation (ICML 2018 · similarity 0.90)
- CGMatch: A Different Perspective of Semi-supervised Learning (CVPR 2025 · similarity 0.87)
- Keep It on a Leash: Controllable Pseudo-label Generation Towards Realistic Long-Tailed Semi-Supervised Learning (NeurIPS 2025 · similarity 0.86)
- OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning (NeurIPS 2024 · similarity 0.85)
- Decouple then Classify: A Dynamic Multi-view Labeling Strategy with Shared and Specific Information (ICML 2024 · similarity 0.85)
- Targeted Representation Alignment for Open-World Semi-Supervised Learning (CVPR 2024 · similarity 0.85)
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