Groups
Neural Collapse describes what happens at the end of training: the penultimate-layer features of each class concentrate tightly around a class mean.
t-SNE and UMAP are nonlinear dimensionality-reduction methods that preserve local neighborhoods to make high-dimensional data visible in 2D or 3D.