Research map / Label-efficient and robust learning / Recognition and long-tail
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add batch normalization after the continuous codes (ablation) | OrthoHash · ImageNet100 | retrieval performance: about 17-20% | Helped |
| replace softmax with sigmoid for multi-label classification (ablation) | OrthoHash · multi-label classification | retrieval performance | Hurt |
| use heuristically generated targets that maximize inter-class Hamming distance (ablation) | OrthoHash · deep hashing at different code lengths | retrieval performance | Mixed |
Closest papers by meaning
- One Loss for Quantization: Deep Hashing With Discrete Wasserstein Distributional Matching (CVPR 2022 · similarity 0.91)
- Pairwise-Label-Based Deep Incremental Hashing with Simultaneous Code Expansion (AAAI 2024 · similarity 0.91)
- Deep Hashing Network for Efficient Similarity Retrieval (AAAI 2016 · similarity 0.91)
- On Trivial Solution and High Correlation Problems in Deep Supervised Hashing (AAAI 2018 · similarity 0.90)
- Deep Supervised Hashing for Fast Image Retrieval (CVPR 2016 · similarity 0.90)
- Statistical Model-driven Similarity Hashing: Bridging Modalities for Efficient Unsupervised Retrieval (AAAI 2025 · similarity 0.87)
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