Research map / Label-efficient and robust learning / Semi-supervised learning
Joint Representative Selection and Feature Learning: A Semi-Supervised Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add source labels to the representative-selection objective (ablation) | Representative selection · MNIST and SCENE15 | recall and accuracy | Helped |
| Add the discriminative term to representative selection (ablation) | Representative selection · MNIST and SCENE15 | recall and accuracy | Helped |
| Use iterative feature learning with target data instead of the two-stage strategy (ablation) | Problem (11) · MNIST and SCENE15 | recall and accuracy | Helped |
| Use the proposed joint representative selection and feature learning method instead of the strongest reported baseline | Representative selection · Narrated instructional videos | recall: 0.637 -> 0.663 | Helped |
| Use the proposed joint representative selection and feature learning method instead of the strongest reported baseline | Representative selection · Narrated instructional videos | accuracy: 0.759 -> 0.794 | Helped |
| Use the proposed joint representative selection and feature learning method instead of the strongest reported baseline | Representative selection · Breakfast | recall: 0.811 -> 0.848 | Helped |
| Use the proposed joint representative selection and feature learning method instead of the strongest reported baseline | Representative selection · Breakfast | accuracy: 0.637 -> 0.680 | Helped |
Closest papers by meaning
- Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection (NeurIPS 2024 · similarity 0.87)
- Estimating Conditional Mutual Information for Dynamic Feature Selection (ICLR 2024 · similarity 0.86)
- Towards a statistical theory of data selection under weak supervision (ICLR 2024 · similarity 0.85)
- SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification (NeurIPS 2024 · similarity 0.85)
- CGMatch: A Different Perspective of Semi-supervised Learning (CVPR 2025 · similarity 0.85)
- Enhanced Sample Selection with Confidence Tracking: Identifying Correctly Labeled Yet Hard-to-Learn Samples in Noisy Data (AAAI 2025 · similarity 0.85)
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