U-net

U-net 用于解决小样本的简单问题分割，比如医疗影片的分割。它遵循的基本原理与FCN一样：

 1. Encoder-Decoder结构： 前半部分为多层卷积池化，不断扩大感受野，用于提取特征。后半部分上采样恢复图片尺寸。

 2. 更丰富的信息融合：如下图中灰色箭头，更多的前后层之间的信息融合。这里是把前面层的输出和后面层串联在一起，区别于FCN的逐元素相加。不同的Feature map串联在一起后，接卷积层，可以让卷积核在channel上自己做出选择。注意的是，在串联之前，需要把前层的feature map crop到和后面层一样的大小。

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