【论文笔记】A Light CNN for Deep Face Representation with Noisy Label

时间 2020-12-30

标签 CNN 繁體版

原文原文链接

摘要第一，提出了对于CNN每一层的maxout activation，得到了Max-Feature-Map(MFM)。MFM区别于ReLU，ReLU通过设置下限封锁神经元，MFM通过竞争关系封锁神经元。MFM不仅能分离噪声信号和信息信号，也可以在特征选取中扮演重要的角色。第二，为了减少参数的数量和改善表现，提出了有5个卷积层和4个NIN层的网络。第三，设计了语义引导方法（semantic b

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1. A Lightened CNN for Deep Face Representation读后感
2. 人脸识别方向论文笔记（1）-- A Light CNN for Deep Face Representation With Noisy Labels
3. 《Learning Deep Representation for Face Alignment with Auxiliary Attributes》论文阅读笔记
4. Learning a Deep ConvNet for Multi-label Classification with Partial Labels 论文笔记
5. Learning Invariant Deep Representation for NIR-VIS Face Recognition
6. A Discriminative Feature Learning Approach for Deep Face Recognition 论文笔记
7. 《A Discriminative Feature Learning Approach for Deep Face Recognition》论文笔记
8. 论文笔记 | A Discriminative Feature Learning Approach for Deep Face Recognition
9. 【multi-label】Learning a Deep ConvNet for Multi-label Classification with Partial Labels
10. 1505.Deep Ranking for Person Re-identification via Joint Representation Learning论文笔记

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