  Neural collapse is an emergent phenomenon in deep learning that was recently
discovered by Papyan, Han and Donoho. We propose a simple "unconstrained
features model" in which neural collapse also emerges empirically. By studying
this model, we provide some explanation for the emergence of neural collapse in
terms of the landscape of empirical risk.
