use average pooling instead of max poolingwhere each local input patch is transformed by taking the average value of each channel over the patchrather than the max but max pooling tends to work better than these alternative solutions in nutshellthe reason is that features tend to encode the spatial presence of some pattern or concept over the different tiles of the feature map (hencethe term feature map)and it' more informative to look at the maximal presence of different features than at their average presence so the most reasonable subsampling strategy is to first produce dense maps of features (via unstrided convolutionsand then look at the maximal activation of the features over small patchesrather than looking at sparser windows of the inputs (via strided convolutionsor averaging input patcheswhich could cause you to miss or dilute feature-presence information at this pointyou should understand the basics of convnets--feature mapsconvolutionand max pooling--and you know how to build small convnet to solve toy problem such as mnist digits classification now let' move on to more usefulpractical applications licensed to