convolutional neural networks ooling operation let us now look at the pooling operationwhich is almost always used in cnns in conjunction with convolution the idea behind the pooling operation is that the exact location of the feature is not concern if in fact it has been discovered it simply provides translation invariance for instanceassume that the task is to learn to detect faces in photographs also assume that the faces in the photographs are tilted (as they generally areand that we have convolutional layer that detects the eyes we would like to abstract the location of the eyes in the photograph from their orientation the pooling operation achieves this and is an important constituent of cnns figure - illustrates the pooling operation for -dimensional input the following points are to be noted the function is commonly the max operation (leading to max pooling)but other variantssuch as average or normcan be used as an alternative for -dimensional inputthis is rectangular portion the output produced as result of pooling is much smaller in dimensionality as compared to the input