論文筆記：Deep Hashing Network for Efficient Similarity Retrieval
論文筆記： Deep Quantization Network for Efficient Image Retrieval 靈魂三問 論文提出的問題 由於quantization error， 哈希編碼將不能準確地表達特徵 論文做了什麼？ 用多池化層卷積神經網絡，來表達圖片 用全連接的哈希層來生成二進制哈希編碼 用相應的交叉熵層來學習相似性 用配對的量化損失來控制哈希質量 論文達到了什麼效果？ 通
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相關文章
- 1. 閱讀小結：Deep Hashing Network for Efficient Similarity Retrieval
- 2. 論文閱讀（6）：Deep Clustering and Block Hashing Network for Face Image Retrieval
- 3. 《SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval》論文筆記
- 4. Deep Supervised Hashing for Fast Image Retrieval
- 5. Hierarchical deep semantic hashing for fast image retrieval
- 6. Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval
- 7. Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-ide
- 8. 【Dynamic Network Surgery for Efficient DNNs】論文筆記
- 9. 論文筆記：Deep Residual Network
- 10. Person Re-identification with Deep Similarity-Guided Graph Neural Network 論文筆記