Learning a Similarity Metric Discriminatively, with Application to Face Verification 論文筆記
0 摘要 我們提出了一種從數據中學習相似性度量的方法。該方法可以用於人臉識別或人臉驗證的應用中。在這些應用中，類別的數量非常大且在訓練期間並不知道，並且每個類別的訓練樣本的數量非常小。這個想法是學習一個能夠將輸入特徵映射到一個目標空間的函數，在這個目標空間中用一個L1範數來度量輸入空間的「距離」。通過最小化loss函數以實現相似性度量。對於同一個人的兩張圖像，度量值較小；對於不同人的
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相關文章
- 1. Learning a Similarity Metric Discriminatively, with Application to Face Verification.
- 2. Learning a Similarity Metric Discriminatively, with Application to Face Verification
- 3. Learning a Similarity Metric Discriminatively, with Application to Face Verification(孿生網絡學習筆記)
- 4. Similarity Metric Learning for Face Recognition2013
- 5. 【論文筆記】Additive Margin Softmax for Face Verification
- 6. 【論文筆記】Deep Metric Learning via Facility Location
- 7. 【論文筆記】On How to Perform a Gold Standard Based Evaluation of Ontology Learning
- 8. 論文筆記_稀疏_聚類:A New Simplex Sparse Learning Model to Measure Data Similarity for Clustering
- 9. 論文閱讀筆記《Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace》
- 10. 《Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning》論文筆記