# Why does Convolutional layer unde usually has the same input/output channel size?

As famous model VGG16 shows(and other famous models), The convolutional layers before pooling usually have the same input and output channel sizes? What's the reason for that? Is there a theory or papers trying to explain this kind of settings?

• What do you mean by input and output channel sizes? – nbro Jun 10 '19 at 13:42

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