Research map / Learning theory and algorithms / Prediction and decision losses
Multilinear Hyperplane Hashing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase multilinear order from 4 to 8 (ablation) | MH · Tiny-1M, 300th active learning iteration | MAP: 38.82±1.03 -> 40.32±0.81 | Helped |
| Increase multilinear order from 4 to 16 (ablation) | MH · Tiny-1M, 300th active learning iteration | MAP: 38.82±1.03 -> 41.67±0.41 | Helped |
| Increase multilinear order from 4 to 8 (ablation) | MH · Tiny-1M, 300th active learning iteration | minimum margin: 7.13±2.11 -> 6.32±1.77 | Helped |
| Increase multilinear order from 4 to 16 (ablation) | MH · Tiny-1M, 300th active learning iteration | minimum margin: 7.13±2.11 -> 4.41±0.83 | Helped |
| Use multilinear hashing instead of AH, EH, BH, or LBH | Active learning with SVMs · MNIST | MAP | Helped |
| Use multilinear hashing instead of AH, EH, BH, or LBH | Active learning with SVMs · Tiny-1M | MAP | Helped |
| Learn projections from data with angular quantization rather than use random projections | Multilinear hyperplane hashing · MNIST | MAP | Helped |
| Increase the lookup radius | Hyperplane hashing on Tiny-1M · Tiny-1M | MAP | Helped |
| Increase the number of hash tables | Hyperplane hashing on Tiny-1M · Tiny-1M | MAP | Helped |
Closest papers by meaning
- Boosting Complementary Hash Tables for Fast Nearest Neighbor Search (AAAI 2017 · similarity 0.86)
- Learning to Hash Robustly, Guaranteed (ICML 2022 · similarity 0.86)
- Fast Online Hashing with Multi-Label Projection (AAAI 2023 · similarity 0.86)
- Approximate Nearest Neighbor Search through Modern Error-Correcting Codes (ICLR 2023 · similarity 0.86)
- An ensemble diversity approach to supervised binary hashing (NeurIPS 2016 · similarity 0.86)
- Sublinear Time Nearest Neighbor Search over Generalized Weighted Space (ICML 2019 · similarity 0.86)
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