Convolutional (Conv) layer

Accepts as input:

Outputs another feature vector of size $W_2&space;\times&space;H_2&space;\times&space;D_2$ , where The d-th channel in the output feature vector is obtained by performing a valid convolution with stride $S$ of the d-th filter and the padded 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 $Q$ by inserting in every one of its channels independently $Q-1$ 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 $H&space;\times&space;W&space;\times&space;D_1$ is convolved with a filter bank of size $H&space;\times&space;W&space;\times&space;D_1&space;\times&space;D_2$ , it results in an output feature vector of size $1&space;\times&space;1&space;\times&space;D_2$ . 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 $1&space;\times&space;1&space;\times&space;D_2&space;\times&space;D_3$ , the result is of size $1&space;\times&space;1&space;\times&space;D_3$ . 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 $H&space;\times&space;W&space;\times&space;D&space;\times&space;C$ , where $C$ is the number of classes. Each one of the filters of size $H&space;\times&space;W&space;\times&space;D$ corresponds to a certain class and there are $C$ classifiers, one for each class.
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