A Discriminative Feature Learning Approach for Deep Face Recognition 論文筆記
0 摘要 卷積神經網絡已被廣泛應用於計算機視覺領域，顯着改善了最先進的技術。在大多數可用的CNN中，使用softmax loss（作爲監督信號）訓練深度模型。爲了增強深度學習特徵的判別力，本文提出了一種新的監督信號，稱爲center loss，用於人臉識別任務。具體而言，center loss爲每個類（在人臉模型中，一個類就對應一個人）指定了一個類別中心。同一類的圖像對應的特徵都應該
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相關文章
- 1. A Discriminative Feature Learning Approach for Deep Face Recognition
- 2. 論文筆記 | A Discriminative Feature Learning Approach for Deep Face Recognition
- 3. 《A Discriminative Feature Learning Approach for Deep Face Recognition》論文筆記
- 4. Center Loss: A Discriminative Feature Learning Approach for Deep Face Recognition（論文閱讀筆記）
- 5. 文獻閱讀 - A Discriminative Feature Learning Approach for Deep Face Recognition
- 6. 閱讀A Discriminative Feature Learning Approach for Deep Face Recognition
- 7. 【人臉識別】A Discriminative Feature Learning Approach for Deep Face Recognition
- 8. 論文筆記（一）Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach
- 9. centerloss系列文章筆記——人臉識別：A Discriminative Feature Learning Approach forDeep Face Recognition
- 10. 論文閱讀理解 - CenterLoss: A Discriminative Feature Learning Approach