對Deep Learning Face Representation from Predicting 10,000 Classes論文的理解
接下來從以下四個方面來介紹我對這篇論文的理解： 一.目的 利用深度學習學習人臉的高維特徵來進行人臉驗證 二.Deep convnets(特徵提取模型) 圖1 卷積網絡模型 結構：包含4個卷積層提取特徵，全連接層即deepid層(160個神經元，由第三卷積層的max_pooling層和第四層卷積層得到),最後一層爲s
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- 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. 【論文整理】DNN-Face-Recognition-Papers DeepID Series
- 5. Learning Invariant Deep Representation for NIR-VIS Face Recognition
- 6. 《Learning Deep Representation for Face Alignment with Auxiliary Attributes》論文閱讀筆記
- 7. LEARNING NOTES FROM DEEP LEARNING
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相關文章
- 1. DeepID1-Deep Learning Face Representation from Predicting 10,000 Classes
- 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. 【論文整理】DNN-Face-Recognition-Papers DeepID Series
- 5. Learning Invariant Deep Representation for NIR-VIS Face Recognition
- 6. 《Learning Deep Representation for Face Alignment with Auxiliary Attributes》論文閱讀筆記
- 7. LEARNING NOTES FROM DEEP LEARNING
- 8. DeepID2：Deep Learning Face Representation by Joint Identification-Verification
- 9. DeepID2 "Deep Learning Face Representation by Joint Identification-Verification"
- 10. (轉) Learning from Imbalanced Classes