1. Why add pooling (pooling) to the convolutional network
If you only use convolutional operations to reduce the size of the feature map, you will lose a lot of information. So think of a way to reduce the volume of stride, leaving most of the information, through pooling to reduce the size of feature map.
Advantages of pooling:
1. Pooled operation does not increase parameters
2. Experimental results show that the model with pooling is more accurate
Disadvantages of pooling:
1. Because the stride of the convolution is reduced, the computational amount is increased
2. At the same time, the pooling layer allows us to add two extra parameters (hyper parameters): Pooling size and pooling stride
2. Why 1x1 convolution
The convolution of the input image is equivalent to a linear classifier, but if you add a 1x1 convolution between the input picture and the convolution layer, the two layers are equivalent to combining a small neural network (nonlinear).
Udacity Google Deep Learning learning Notes