論文閱讀（9）：AdapticeFace: Adaptive Margin and Sampling for Face Recognition
本文是對Margin based Softmax Loss的改進。主要針對人臉數據集類別不平衡的問題，以及難類別/難樣本的挖掘，讓訓練過程變得高效。html 1、針對的問題web SOTA Margin based Softmax Loss都有一個前提假設：即全部的類別都有足夠多的樣原本描述該類別的分佈，所以一個預設的常量margin就足以縮小每個類別的類內差別。實際上人臉數據集的樣本很不
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相關文章
- 1. 論文閱讀（9）：AdapticeFace: Adaptive Margin and Sampling for Face Recognition
- 2. AdaptiveFace: Adaptive Margin and Sampling for Face Recognition 閱讀筆記
- 3. 論文閱讀筆記：ArcFace: Additive Angular Margin Loss for Deep Face Recognition
- 4. FaceNet: A Unified Embedding for Face Recognition and Clustering（論文閱讀筆記）
- 5. 論文閱讀 FaceNet: A Unified Embedding for Face Recognition and Clustering
- 6. 文獻閱讀 - Deep Face Recognition
- 7. 文獻閱讀 - FaceNet: A Unified Embedding for Face Recognition and Clustering
- 8. SphereFace: Deep Hypersphere Embedding for Face Recognition論文閱讀
- 9. 【ArcFace/Insightface論文中文版】ArcFace: Additive Angular Margin Loss for Deep Face Recognition
- 10. 論文閱讀（5）：Age invariant face recognition and retrieval by coupled auto-encoder networks