Are there any methods of regularisation of deep neural networks, particularly CNNs (or generally ANN but that will also work on CNNs) that are related only to the network's architecture and not the training itself?
I mean maybe something like how deep they are, amount of conv/pooling/fully connected layers, size of filters, size of steps of filters, etc. any pointers that would help with regularisation.
EDIT: To explain deeper what I mean I might add that I am exploring an experimental idea for the training of the CNNs that is not in any way related to typical gradient descent with backpropagation. That is why typical methods related to training will not work. I can see already that the models train satisfactorily on the training set but don't perform that well on a test set and since I didn't figure out any regularization methods for this type of training I thought maybe there are some related to architecture, that the training process will have to abide.