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Here's the famous VGG-16 model.

VGG16

Do the inputs and outputs of a convolutional layer, before pooling, usually have the same depth? What's the reason for that?

Is there a theory or paper trying to explain this kind of setting?

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  • $\begingroup$ I edited this post in order to save it. It wasn't clear what you meant by "input/output channels". To answer what I think is your question: no, the depth of the inputs and outputs of a convolutional layer are not typically the same. $\endgroup$
    – nbro
    Jul 4 '20 at 20:32
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Keeping the same channel size allows the model to maintain rank but i would say the main reason is convenience. Its easier book keeping.

Also in many model cases output features need some form of alignment with the input (example being all models using residual units -- $\hat{x} = F(x) + x$

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  • $\begingroup$ What do you mean by "rank" in this context? It is also not clear the question because the depth of the input and output volumes are usually different. $\endgroup$
    – nbro
    Jun 10 '19 at 17:11
  • $\begingroup$ also good point about alignment because GPU and CPU vector ops need this $\endgroup$ Jun 10 '19 at 17:54

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