Update public notes

Author: actions-user

content/U-Net 论文阅读笔记.md

@@ -22,4 +22,19 @@
 
 典型的卷积网络执行的图像分类任务都是给一张图片一个类标签。但是呢，在某些领域，比如说生物图像处理领域，所需要的是精确到像素级别的分类。并且，在生物医学领域想要搞到成千上万的训练图几乎是不可能的。
 
-随后就有人使用了滑动窗口+卷积网络来进行像素级别的分类。
+	First, this network can localize. Secondly, the training data in terms
+	of patches is much larger than the number of training images. The resulting
+	network won the EM segmentation challenge at ISBI 2012 by a large margin.
+	Obviously, the strategy in Ciresan et al. [1] has two drawbacks. First, it
+	is quite slow because the network must be run separately for each patch, and
+	there is a lot of redundancy due to overlapping patches. Secondly, there is a
+	trade-off between localization accuracy and the use of context. Larger patches
+	require more max-pooling layers that reduce the localization accuracy, while
+	small patches allow the network to see only little context. More recent approches proposed a classifier output that takes into account the features from
+	multiple layers. Good localization and the use of context are possible at the
+	same time.
+
+随后就有人使用了滑动窗口+卷积网络来进行像素级别的分类。*（此处没读懂）*
+
+这个网络有两个缺点：速度慢，在定位的时候没有很好的利用图片上下文（也就是局部的图片信息，因为用了池化层，导致全局信息不能和局部信息连接，同时局部信息也不能和全局信息连接）
+