introduction to convnets understanding convolution strides the other factor that can influence output size is the notion of strides the description of convolution so far has assumed that the center tiles of the convolution windows are all contiguous but the distance between two successive windows is parameter of the convolutioncalled its stridewhich defaults to it' possible to have strided convolutionsconvolutions with stride higher than in figure you can see the patches extracted by convolution with stride over input (without paddingfigure convolution patches wit st rides using stride means the width and height of the feature map are downsampled by factor of (in addition to any changes induced by border effectsstrided convolutions are rarely used in practicealthough they can come in handy for some types of modelsit' good to be familiar with the concept to downsample feature mapsinstead of strideswe tend to use the max-pooling operationwhich you saw in action in the first convnet example let' look at it in more depth the max-pooling operation in the convnet exampleyou may have noticed that the size of the feature maps is halved after every maxpooling layer for instancebefore the first maxpooling layersthe feature map is but the max-pooling operation halves it to that' the role of max poolingto aggressively downsample feature mapsmuch like strided convolutions max pooling consists of extracting windows from the input feature maps and outputting the max value of each channel it' conceptually similar to convolutionexcept that instead of transforming local patches via learned linear transformation (the convolution kernel)they're transformed via hardcoded max tensor operation big difference from convolution is that max pooling is usually done with windows and licensed to