**Convolutional (Conv) layer**

Accepts as input:

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**Stride**

The amount by which a filter shifts spatially when convolving it with a feature vector.

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**Dilation**

A filter is dilated by a factor
by inserting in every one of its channels independently
zeros between the filter elements.

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**Fully connected (FC) layer**

In practice, FC layers are implemented using a convolutional layer.
To see how this might be possible, note that when an input feature vector of size
is convolved with a filter bank of size
, it results in an output feature vector of size
.
Since the convolution is valid and the filter can not move spatially, the operation is equivalent to a fully connected one.
More over, when this feature vector of size 1x1xD_2 is convolved with another filter bank of size
, the result is of size
.
In this case, again, the convolution is done over a single spatial location and therefore equivalent to a fully connected layer.

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**Linear classifier**

This is implemented in practice by employing a fully connected layer of size
, where
is the number of classes.
Each one of the filters of size
corresponds to a certain class and there are
classifiers, one for each class.

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