Groups
t-SNE and UMAP are nonlinear dimensionality-reduction methods that preserve local neighborhoods to make high-dimensional data visible in 2D or 3D.
Disentangled representations aim to encode independent factors of variation (like shape, size, or color) into separate coordinates of a latent vector.