Groups
Category
Manifold learning assumes high-dimensional data actually lies near a much lower-dimensional, smoothly curved surface embedded in a higher-dimensional space.
Disentangled representations aim to encode independent factors of variation (like shape, size, or color) into separate coordinates of a latent vector.