Research map / Label-efficient and robust learning / Semi-supervised learning
Semi-Supervised Metric Learning: A Deep Resurrection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add an orthogonality constraint on metric parameters (ablation) | Ours · MNIST | NMI: 42.9 -> 47.5 | Helped |
| add an orthogonality constraint on metric parameters (ablation) | Ours · Fashion-MNIST | NMI: 50.3 -> 52.1 | Helped |
| add an orthogonality constraint on metric parameters (ablation) | Ours · CIFAR-10 | NMI: 19.0 -> 25.3 | Helped |
| vary the angular parameter alpha within 35°–55° (ablation) | Ours · Cars-196 | performance | No clear effect |
Closest papers by meaning
- Unsupervised Metric Learning with Synthetic Examples (AAAI 2020 · similarity 0.88)
- Deep Metric Learning with Graph Consistency (AAAI 2021 · similarity 0.88)
- Distance Metric Learning with Joint Representation Diversification (ICML 2020 · similarity 0.88)
- Self-Taught Metric Learning Without Labels (CVPR 2022 · similarity 0.87)
- Divide and Conquer the Embedding Space for Metric Learning (CVPR 2019 · similarity 0.87)
- Deep Disentangled Metric Learning (AAAI 2025 · similarity 0.85)
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