【2017_ICCV】Deep Metric Learning with Angular Loss
提出了Angular Loss，考慮角度關係作爲相似性度量。之前的度量方法主要考慮優化相似性（ContrastIve）或者相對相似性（Triplet Loss），文中方法限制negative point of the triplet triangles之間的角度。 優點：引入尺度不變，提高了目標對於特徵差異的魯棒性；增加了三階幾何限制，捕獲了triplet triangles的附加局部結構；收斂更
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相關文章
- 1. Ranked List Loss for Deep Metric Learning
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- 3. Deep Metric Learning with Hierarchical Triplet Loss 閱讀筆記
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- 5. 度量學習 度量函數 metric learning deep metric learning 深度度量學習
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