DeepID1-Deep Learning Face Representation from Predicting 10,000 Classes
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。
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
- 4. (轉) Learning from Imbalanced Classes
- 5. Self-Supervised Representation Learning
- 6. [導讀]Learning from Imbalanced Classes
- 7. Predictive learning vs. representation learning
- 8. Learning Invariant Deep Representation for NIR-VIS Face Recognition
- 9. DeepID2：Deep Learning Face Representation by Joint Identification-Verification
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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
- 4. (轉) Learning from Imbalanced Classes
- 5. Self-Supervised Representation Learning
- 6. [導讀]Learning from Imbalanced Classes
- 7. Predictive learning vs. representation learning
- 8. Learning Invariant Deep Representation for NIR-VIS Face Recognition
- 9. DeepID2：Deep Learning Face Representation by Joint Identification-Verification
- 10. DeepID2 "Deep Learning Face Representation by Joint Identification-Verification"