actual training signal all right so they give some theory to it that it flattens representations and specifically they say under some conditions namely if the network is large enough so if the dimension of the hidden representation is of a certain size then if you optimize this manifold mix up like if you optimize over every London over the entire training data set what you will end up is actually a linear rally near function of the input this is not too surprising that if you because what you do is you mix linearly