I am wondering whether there is a general statement of the sort "earlier layers in neural networks learn more concepts/features than later layers" or the other way around.
The output layer not being taken into account, as it should learn as many concepts as there are classes (in a classification task).
Are there any resources or papers which tackled this questions, maybe in image classification?
thanks