Research map / Learning theory and algorithms / Sample complexity
Maximum Consensus by Weighted Influences of Monotone Boolean Functions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Bernoulli weighted influences instead of MBF's influence sampling | MaxCon fitting · KITTI fundamental matrix image pairs | consensus: 271.70 vs 271.55; 287.70 vs 288.35; 358.95 vs 359.05; 507.15 vs 507.65; 444.20 vs 444.15 | Mixed |
| Use Bernoulli weighted influences instead of MBF's influence sampling | MaxCon fitting · KITTI fundamental matrix image pairs | runtime (s): 3.17 vs 2.64; 1.88 vs 1.75; 2.36 vs 2.10; 3.61 vs 3.42; 3.19 vs 3.09 | Helped |
| Use Bernoulli weighted influences instead of time-matched RANSAC | MaxCon fitting · KITTI fundamental matrix image pairs | consensus: 266.55 vs 271.55; 284.00 vs 288.35; 353.30 vs 359.05; 497.05 vs 507.65; 432.60 vs 444.15 | Helped |
| Add local expansion (ablation) | WI · KITTI fundamental matrix image pairs | consensus | Helped |
| Remove local expansion (ablation) | WI · KITTI fundamental matrix image pairs | consensus: 269.50; 287.40; 357.40; 504.75; 441.90 | Hurt |
| Increase the number of Bernoulli influence samples | WI influence estimation · Synthetic 2D line fitting | normalized SF distance | Helped |
| Set Bernoulli sampling probability q between (p+1)/n and 0.4 | WI influence estimation · Synthetic 2D line fitting | normalized SF distance | Helped |
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