9/7（CVPR18）Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square
不斷學習得到一個表達能力強的feature，表示圖像時候做了個全局pooling,相當於取了一個均值，從統計的意義上說均值只是一階的信息。 深度分類網絡主要分爲兩個部分：特徵提取和分類器。無論是VGG還是GoogleNet，後來的Resnet、Densenet，仔細觀察可以發現，無論設計了多麼性能優異的網絡，在連接分類器之前，一般都連接了一個Pooling層，如下表所示： 無論是Average P
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相關文章
- 1. Towards Open-Set Identity Preserving Face Synthesis（CVPR18）
- 2. 【Faster RCNN】《Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks》
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- 8. Object Detection Summary
- 9. Global Average Pooling與FC與Average Pooling與 Global Max Pooling
- 10. DANN：Domain-Adversarial Training of Neural Networks