1. Reduce parameters. By reducing the dimension of feature map, the parameters required for subsequent layers are effectively reduced

2. Translation variance. It means that for input, when the pixels in the neighborhood are slightly displaced, the output of the pooling layer is unchanged. This enhances the robustness of the network and has a certain anti disturbance effect (translation invariance)

The error of feature extraction mainly comes from two aspects:

(1) The variance of the estimated value increases due to the limited size of the neighborhood;

(2) The convolution layer parameter error causes the offset of the estimated mean.

Generally speaking, mean pooling can reduce the first error and retain more background information of the image. Max pooling can reduce the second error and retain more texture information.