Research map / Label-efficient and robust learning / Domain adaptation and generalization
Cold-Start Heterogeneous-Device Wireless Localization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase lambda1 (weight of zero-sum constraint regularizer) (ablation) | Ours · Four device pairs | accuracy | Helped |
| Vary lambda2 from small values (ablation) | Ours · Four device pairs | accuracy | No clear effect |
| Set lambda2 to 100 (ablation) | Ours · Four device pairs | accuracy | Hurt |
| Tune d2 up to 150 (ablation) | Ours · Four device pairs | accuracy | Helped |
| Use proposed model instead of ECL, the best baseline | Ours · Four device pairs, 4 m error | relative accuracy: 23.1%-91.3% | Helped |
| Use proposed model instead of SVM | Ours · Four device pairs | accuracy | Helped |
| Use pairwise RSS ratio features (HLF) instead of SVM | HLF · Four device pairs | accuracy | Mixed |
| Vary error distance 1 to 5 meters | Ours · Four device pairs | accuracy | Helped |
| Reduce source training data from 100% to 20% | Ours · Four device pairs, 4 m error | accuracy | Helped |
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