Research map / Learning theory and algorithms / Prediction and decision losses
Towards Optimal Discrete Online Hashing with Balanced Similarity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use BSODH instead of MIHash | 64-bit supervised online hashing · CIFAR-10 | mAP: 5.11% | Helped |
| Use BSODH instead of MIHash | 64-bit supervised online hashing · CIFAR-10 | Precision@H2: 29.97% | Helped |
| Use BSODH instead of MIHash | 64-bit supervised online hashing · Places205 | mAP@1,000: 1.40% | No clear effect |
| Use BSODH instead of MIHash | 64-bit supervised online hashing · Places205 | Precision@H2: 2.63% | Helped |
| Use BSODH instead of MIHash | 64-bit supervised online hashing · MNIST | mAP: 6.48% | Helped |
| Use BSODH instead of MIHash | 64-bit supervised online hashing · MNIST | Precision@H2: 9.2% | Helped |
| Replace balanced similarity with unbalanced similarity (η_s = 1, η_d = 1) (ablation) | BSODH · MNIST, 64-bit | Precision@H2: 0.814 -> 0.206 | Hurt |
| Scale σ to 10,000 instead of using balanced similarity (ablation) | BSODH · MNIST, 64-bit | Precision@H2: 0.814 -> 0.341 | Hurt |
Closest papers by meaning
- One Loss for Quantization: Deep Hashing With Discrete Wasserstein Distributional Matching (CVPR 2022 · similarity 0.89)
- Online Hashing with Efficient Updating of Binary Codes (AAAI 2020 · similarity 0.89)
- SADIH: Semantic-Aware DIscrete Hashing (AAAI 2019 · similarity 0.89)
- Fast Online Hashing with Multi-Label Projection (AAAI 2023 · similarity 0.88)
- Generalized Debiased Semi-Supervised Hashing for Large-Scale Image Retrieval (AAAI 2025 · similarity 0.87)
- Deep Graph Online Hashing for Multi-Label Image Retrieval (AAAI 2025 · similarity 0.87)
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