Learning a Similarity Metric Discriminatively, with Application to Face Verification
Abstract 我們提出了一種通過從數據中進行相似度度量訓練的方法。本方法可以用在識別或者認證應用，這些應用中類別很多且訓練時未知，並且單個類別的訓練數據數量很少。主要的思想是學習一個函數，將輸入映射到目標空間中，使得目標空間中的L1距離基本與輸入空間中的語義特徵一致。這種方法應用到了人臉認證任務中。學習過程最小化具有區分度的損失函數，使得兩個來自相同人的人臉相似度度量變小，不同人的人臉相似
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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 論文筆記
- 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. Circle Loss: A Unified Perspective of Pair Similarity Optimization
- 6. Few-shot Learning with Meta Metric Learners
- 7. Large-scale Distance Metric Learning with Uncertainty
- 8. DeepID2：Deep Learning Face Representation by Joint Identification-Verification
- 9. 【2017_ICCV】Deep Metric Learning with Angular Loss
- 10. A Gentle Guide to Machine Learning