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DeepID1-Deep Learning Face Representation from Predicting 10,000 Classes
時間 2021-01-07
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特色: 1.Multi-scale Convnets:網絡的deepid層連接第三層卷積與第四層卷積,文章稱之爲Multi-scale Convnets,使用這種方式可以提取到第三層的低層特徵以及第四層的高層特徵。因此網絡可以提取到更有效的特徵向量DeepID。文中證明使用Multi-scale Convnets相比不使用可以降低驗證集的錯誤率,即分類錯誤率。
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相關文章
1.
對Deep Learning Face Representation from Predicting 10,000 Classes論文的理解
2.
DeepID1翻譯:Deep Learning Face Representation from Predicting 10,000 Classes
3.
Deep Learning Face Representation from Predicting 10,000 Classes - 閱讀筆記 -人臉特徵(DeepID)提取與識別
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(轉) Learning from Imbalanced Classes
5.
Self-Supervised Representation Learning
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[導讀]Learning from Imbalanced Classes
7.
Predictive learning vs. representation learning
8.
Learning Invariant Deep Representation for NIR-VIS Face Recognition
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