Understanding pooling layers

A final consideration when using convolutional layers is to do with the idea of stacking simple cells to detect local patterns and complex cells to downsample representations, as we saw earlier with the cat-brain experiments, and the neocognitron. The convolutional filters we saw behave like simple cells by focusing on specific locations on the input and training neurons to fire, given some stimuli from the local regions of our input image. Complex cells, on the other hand, are required to be less specific to the location of the stimuli. This is where the pooling layer comes in. This technique of pooling intends to reduce the output of CNN layers to more manageable representations. Pooling layers are periodically added between convolutional layers to spatially downsample the outputs of our convolutional layer. All this does is progressively reduce...