Pooling 的作用及优缺点

Pooling 层作用： 
1.invariance(translation, rotation, scale)
2. 保留主要特征的同事减少参数和计算量（降维），提高模型的泛化能力

 在神经网络中，池化函数一般在卷积函数下一层

池化操作是利用一个矩阵窗口在张量上进行扫描，将每个矩阵窗口中的值通过取最大值或者平均值来减少元素个数。

 avg_pool, max_pool, max_pool_with_argmax

 尺度不变性增大了感受野， 让卷积看到更多的信息，但是增大感受野的前提是降低分辨率，丢失了重要信息，这对segmentation要求的精确度location有一定的影响。
但为了增大感受野一开始就用跟图像一样大的卷积核，这样感受野不就变大了吗？
错，卷积层越深模型的表征能力越强，但此时降维会导致重要信息丢失。

 Adopt from https://blog.csdn.net/zxyhhjs2017/article/details/78607469

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