论文笔记：Deep Hashing Network for Efficient Similarity Retrieval
论文笔记： Deep Quantization Network for Efficient Image Retrieval 灵魂三问 论文提出的问题 由于quantization error， 哈希编码将不能准确地表达特征 论文做了什么？ 用多池化层卷积神经网络，来表达图片 用全连接的哈希层来生成二进制哈希编码 用相应的交叉熵层来学习相似性 用配对的量化损失来控制哈希质量 论文达到了什么效果？ 通
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相关文章
- 1. 阅读小结：Deep Hashing Network for Efficient Similarity Retrieval
- 2. 论文阅读（6）：Deep Clustering and Block Hashing Network for Face Image Retrieval
- 3. 《SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval》论文笔记
- 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】论文笔记
- 9. 论文笔记：Deep Residual Network
- 10. Person Re-identification with Deep Similarity-Guided Graph Neural Network 论文笔记