对Deep Learning Face Representation from Predicting 10,000 Classes论文的理解
接下来从以下四个方面来介绍我对这篇论文的理解： 一.目的 利用深度学习学习人脸的高维特征来进行人脸验证 二.Deep convnets(特征提取模型) 图1 卷积网络模型 结构：包含4个卷积层提取特征，全连接层即deepid层(160个神经元，由第三卷积层的max_pooling层和第四层卷积层得到),最后一层为s
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相关文章
- 1. DeepID1-Deep Learning Face Representation from Predicting 10,000 Classes
- 2. DeepID1翻译：Deep Learning Face Representation from Predicting 10,000 Classes
- 3. Deep Learning Face Representation from Predicting 10,000 Classes - 阅读笔记 -人脸特征（DeepID）提取与识别
- 4. 【论文整理】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》论文阅读笔记
- 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. (转) Learning from Imbalanced Classes