I have just read the paper from Ian Goodfellow et al. titled "Maxout Networks".
It seems that the Maxout activation should be quite powerful, as it can approximate any convex function, i.e. Relu, function that is used in many state-of-the-art models. Also, Maxout, in theory, should work better with dropout than other activation functions.
I am therefore wondering, what is the intuition for using Relu instead of Maxout.