Research map / Label-efficient and robust learning / Recognition and long-tail
Deep Hashing Network for Efficient Similarity Retrieval
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| DHN (pairwise cross-entropy + quantization loss) vs DNNH | deep supervised hashing · NUS-WIDE, CIFAR-10, Flickr | average MAP: +3.8%, +2.9%, +3.6% | Helped |
| add pairwise quantization loss (ablation) | DHN · NUS-WIDE, CIFAR-10, Flickr | MAP: 0.703 -> 0.758 (NUS-WIDE 48 bits) | Helped |
| use pairwise cross-entropy loss instead of pairwise squared loss (ablation) | DHN · NUS-WIDE, CIFAR-10, Flickr | average MAP: +9.07%, +8.01%, +6.64% | Helped |
| binarize codes (vs continuous embeddings) (ablation) | DHN · NUS-WIDE 48 bits | MAP: 0.789 -> 0.758 | Hurt |
| deep end-to-end hashing vs best shallow method KSH | hashing · NUS-WIDE, CIFAR-10, Flickr | average MAP: +16.3%, +25.8%, +12.7% | Helped |
Closest papers by meaning
- Deep Quantization Network for Efficient Image Retrieval (AAAI 2016 · similarity 0.97)
- Deep Supervised Hashing for Fast Image Retrieval (CVPR 2016 · similarity 0.95)
- Supervised Deep Hashing for Hierarchical Labeled Data (AAAI 2018 · similarity 0.92)
- HashGAN: Deep Learning to Hash With Pair Conditional Wasserstein GAN (CVPR 2018 · similarity 0.92)
- Pairwise-Label-Based Deep Incremental Hashing with Simultaneous Code Expansion (AAAI 2024 · similarity 0.90)
- Deep Graph Online Hashing for Multi-Label Image Retrieval (AAAI 2025 · similarity 0.89)
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