論文閱讀理解 - Deep Metric Learning via Lifted Structured Feature Embedding
Deep Metric Learning via Lifted Structured Feature Embedding Paper Caffe-Code 摘要 - 提出一種樣本間距離度量方法，其出發點在於，(一)學習樣本語義特徵嵌入，使得在語義嵌入空間中，相似樣本映射距離更接近，不相似樣本映射距離更遠. (二)更好的利用網絡訓練中 batch 訓練的優勢，提出將一個 batch 內樣本的成對距離
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相關文章
- 1. 論文閱讀：Deep Metric Learning via Lifted Structured Feature Embedding
- 2. 【論文筆記】Deep Metric Learning via Facility Location
- 3. 論文閱讀：Collaborative Deep Embedding Via Dual Networks
- 4. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space論文解讀
- 5. Machine Learning & Deep Learning 論文閱讀筆記
- 6. 論文閱讀：（AFM）Learning the Weight of Feature Interactions via Attention Networks
- 7. 論文閱讀：《Wide & Deep Learning for Recommender Systems》
- 8. 論文閱讀 Deep Attentional Structured Representation Learning for Visual Recognition
- 9. 論文閱讀：《Structured Feature Learning for Pose Estimation》CVPR 2016
- 10. 論文閱讀：CVPR2016 Paper list