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Column Sampling Based Discrete Supervised Hashing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| discrete optimization with column sampling (COSDISH) vs continuous-relaxation LFH | supervised hashing · CIFAR-10 8-bit | MAP: 0.2908 -> 0.4986 | Helped |
| COSDISH vs LFH | supervised hashing · CIFAR-10 64-bit | MAP: 0.6182 -> 0.6371 | Helped |
| COSDISH BT vs FastH (both boosted trees) | supervised hashing · NUS-WIDE 64-bit | MAP: 0.5736 -> 0.6786 | Helped |
| boosted decision trees instead of linear classifier for out-of-sample extension (ablation) | COSDISH · CIFAR-10 64-bit | MAP: 0.6371 -> 0.7346 | Helped |
| COSDISH vs LFH training cost | supervised hashing · NUS-WIDE 200K | training time (s): 85.9 -> 162.2 | Hurt |
| use LBFGSB continuous solver for the BQP subproblem (ablation) | COSDISH · CIFAR-10/NUS-WIDE | MAP | Hurt |
Closest papers by meaning
- Deep Supervised Discrete Hashing (NeurIPS 2017 · similarity 0.89)
- Generalized Debiased Semi-Supervised Hashing for Large-Scale Image Retrieval (AAAI 2025 · similarity 0.88)
- Discretely Coding Semantic Rank Orders for Supervised Image Hashing (CVPR 2017 · similarity 0.88)
- SADIH: Semantic-Aware DIscrete Hashing (AAAI 2019 · similarity 0.87)
- Discrete Image Hashing Using Large Weakly Annotated Photo Collections (AAAI 2016 · similarity 0.87)
- Distribution-Consistency-Guided Multi-modal Hashing (AAAI 2025 · similarity 0.85)
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