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In CNN, we will learn filters to produce feature map in convolutional layer.

In Autoencoder, each layer's single hidden unit can be considered as filter.

What the difference between the filters learned in these two network?

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In case of CNN filters are applied to small patches of an image at each possible location (which also makes them translation invariant).

Autoencoder's hidden layers get whole image (output of the previous layer) as their input, which doesn't look like a good idea for images: usually only spatially local features correlate, whereas more distant ones are less correlated. Also, these hidden neurons are not translation invariant.

Thus, CNNs are like usual ANNs with a special kind of regularization, which zeros out most of weights to make use of locality.

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