  Neural collapse describes the geometry of activation in the final layer of a
deep neural network when it is trained beyond performance plateaus. Open
questions include whether neural collapse leads to better generalization and,
if so, why and how training beyond the plateau helps. We model neural collapse
as an information bottleneck (IB) problem in order to investigate whether such
a compact representation exists and discover its connection to generalization.
We demonstrate that neural collapse leads to good generalization specifically
when it approaches an optimal IB solution of the classification problem. Recent
research has shown that two deep neural networks independently trained with the
same contrastive loss objective are linearly identifiable, meaning that the
resulting representations are equivalent up to a matrix transformation. We
leverage linear identifiability to approximate an analytical solution of the IB
problem. This approximation demonstrates that when class means exhibit
$K$-simplex Equiangular Tight Frame (ETF) behavior (e.g., $K$=10 for CIFAR10
and $K$=100 for CIFAR100), they coincide with the critical phase transitions of
the corresponding IB problem. The performance plateau occurs once the optimal
solution for the IB problem includes all of these phase transitions. We also
show that the resulting $K$-simplex ETF can be packed into a $K$-dimensional
Gaussian distribution using supervised contrastive learning with a ResNet50
backbone. This geometry suggests that the $K$-simplex ETF learned by supervised
contrastive learning approximates the optimal features for source coding.
Hence, there is a direct correspondence between optimal IB solutions and
generalization in contrastive learning.
