9/7（CVPR18）Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square
不断学习得到一个表达能力强的feature，表示图像时候做了个全局pooling,相当于取了一个均值，从统计的意义上说均值只是一阶的信息。 深度分类网络主要分为两个部分：特征提取和分类器。无论是VGG还是GoogleNet，后来的Resnet、Densenet，仔细观察可以发现，无论设计了多么性能优异的网络，在连接分类器之前，一般都连接了一个Pooling层，如下表所示： 无论是Average P
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相关文章
- 1. Towards Open-Set Identity Preserving Face Synthesis（CVPR18）
- 2. 【Faster RCNN】《Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks》
- 3. Covariance Matrix 协方差矩阵
- 4. MATLAB判别分析报错：The covariance matrix of each group in TRAINING must be positive definite.
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- 6. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks （faster rcnn 论文翻译）
- 7. [cv231n] Lecture 6 | Training Neural Networks I (data processing&weight initializing&batch norm)
- 8. Object Detection Summary
- 9. Global Average Pooling与FC与Average Pooling与 Global Max Pooling
- 10. DANN：Domain-Adversarial Training of Neural Networks