Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Bayesian Supervised Hashing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use fully Bayesian variational inference with automatically tuned hyperparameters instead of MAP-style estimation | supervised hashing · ESPGAME, IAPRTC12, NUS-WIDE-GND, and NUS-WIDE-TAG | mAP | Helped |
| Use fully Bayesian variational inference with automatically tuned hyperparameters instead of MAP-style estimation | supervised hashing · NUS-WIDE-GND, 64 bits | mAP: 0.5359 -> 0.5256 | Hurt |
| Select 32 bits from an already learned 64-bit code using ARD-ranked hyperparameters, rather than learning 32 bits directly (ablation) | BSH · ESPGAME, IAPRTC12, NUS-WIDE-GND, and NUS-WIDE-TAG | mAP: 1% declination of mAP | Hurt |
Closest papers by meaning
- Supervised Deep Hashing for Hierarchical Labeled Data (AAAI 2018 · similarity 0.88)
- Optimizing affinity-based binary hashing using auxiliary coordinates (NeurIPS 2016 · similarity 0.88)
- Generalized Debiased Semi-Supervised Hashing for Large-Scale Image Retrieval (AAAI 2025 · similarity 0.88)
- DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs (CVPR 2019 · similarity 0.88)
- KALAHash: Knowledge-Anchored Low-Resource Adaptation for Deep Hashing (AAAI 2025 · similarity 0.87)
- Effective Comparative Prototype Hashing for Unsupervised Domain Adaptation (AAAI 2024 · similarity 0.85)
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