After a convolution operation, a pooling operation is generally performed to reduce dimensionality and the number of parameters to be learned, which shortens the training time, requires less data to train, and combats overfitting. Pooling layers downsample each feature map independently, reducing the height and width, but keeping the depth intact. The most common type of pooling is max pooling, which just takes the maximum value in the pooling window. Contrary to the convolution operation, pooling has no parameters. It slides a window over its input and simply takes the max value in the window. Similar to a convolution, the window size and stride for pooling can be specified.