The output features maps can be sensitive to the location of the features in the input. To overcome this problem, pooling layers have been introduced. Pooling layers select specific values on the features maps and pass through the subsequent layers.  This has the effect of making the resulting down-sampled feature maps more robust to changes in the position of the feature in the image, referred to by the technical phrase “local translation invariance.” Pooling layers provide an approach to downsampling feature maps by summarizing the presence of features in patches of the feature map. Two common pooling methods are average pooling and max pooling that summarize the average presence of a feature and the most activated presence of a feature respectively.