Research map / Label-efficient and robust learning / Recognition and long-tail
SADIH: Semantic-Aware DIscrete Hashing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the L1-norm loss for binary-code updates instead of the L21-norm loss (ablation) | SADIH · CIFAR-10, SUN397, and ImageNet | MAP: 0.7319 -> 0.7348 (CIFAR-10); 0.7655 -> 0.8077 (SUN397); 0.8082 -> 0.8165 (ImageNet) | Mixed |
| Use the L1-norm loss for binary-code updates instead of the L21-norm loss (ablation) | SADIH · CIFAR-10 | computation time (seconds): 315.40 -> 0.97 | Helped |
| Use the L1-norm loss for binary-code updates instead of the L21-norm loss (ablation) | SADIH · SUN397 | computation time (seconds): 695.19 -> 11.79 | Helped |
| Use the L1-norm loss for binary-code updates instead of the L21-norm loss (ablation) | SADIH · ImageNet | computation time (seconds): 732.02 -> 8.03 | Helped |
Closest papers by meaning
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- KALAHash: Knowledge-Anchored Low-Resource Adaptation for Deep Hashing (AAAI 2025 · similarity 0.88)
- Statistical Model-driven Similarity Hashing: Bridging Modalities for Efficient Unsupervised Retrieval (AAAI 2025 · similarity 0.88)
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