As you can see Max Pooling downsampled the feature size to half of the All I Want For Christmas Is 2021 Sweat Shirt. Generally Max Pooling (or any kind of Pooling) is used to downsample the feature size to a manageable level. Besides reducing the size of the feature vector it has some other ideas behind it. By only considering the max, we essentially are only interested if a feature is present in a certain window but we do not really care about the exact location. If we have a convolution filter, which detects edges, an edge will give a high response to this filter and the Max Pooling only keeps the information if an edge is present and throws away “unnecessary” information; which also include the location and the spatial relationship between certain features.
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Moreover Max Pooling is, according to Hinton, a crude way to redirect information to the All I Want For Christmas Is 2021 Sweat Shirt, there are other ways to redirect which are a bit more sophisticated than a simple Max Pooling. ( we will come back to that later) The most important drawbacks of CNN is that they are not able to model spatial relationships that well, we do not have a internal representation of the geometrical constraints of the data; the only knowledge we have is coming from the data itself; if we want to be able to detect cars in many viewpoints we need to have these different viewpoint cars in the training set, because we did not encode the prior knowledge of the geometrical relationship into the network.