Spatial Pooling can be of different types – max pooling, average pooling, and Sum pooling.
- Max pooling: Once we obtain the feature map of the input, we will apply a filter of determined shapes across the feature map to get the maximum value from that portion of the feature map. It is also known as subsampling because from the entire portion of the feature map covered by filter or kernel we are sampling one single maximum value.
- Average pooling: Computes the average value of the feature map covered by kernel or filter, and takes the floor value of the result.
- Sum pooling: Computes the sum of all elements in that window.
Max pooling returns the maximum value of the portion covered by the kernel and suppresses the Noise, while Average pooling only returns the measure of that portion.
The most widely used pooling technique is max pooling since it captures the features of maximum importance with it.
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