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A Discriminative Feature Learning Approach for Deep Face Recognition 論文筆記
時間 2020-12-29
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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
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