  Neural collapse (NC) refers to the surprising structure of the last layer of
deep neural networks in the terminal phase of gradient descent training.
Recently, an increasing amount of experimental evidence has pointed to the
propagation of NC to earlier layers of neural networks. However, while the NC
in the last layer is well studied theoretically, much less is known about its
multi-layered counterpart - deep neural collapse (DNC). In particular, existing
work focuses either on linear layers or only on the last two layers at the
price of an extra assumption. Our paper fills this gap by generalizing the
established analytical framework for NC - the unconstrained features model - to
multiple non-linear layers. Our key technical contribution is to show that, in
a deep unconstrained features model, the unique global optimum for binary
classification exhibits all the properties typical of DNC. This explains the
existing experimental evidence of DNC. We also empirically show that (i) by
optimizing deep unconstrained features models via gradient descent, the
resulting solution agrees well with our theory, and (ii) trained networks
recover the unconstrained features suitable for the occurrence of DNC, thus
supporting the validity of this modeling principle.
