Within a manifold, it is always possible to interpolate between two inputs by morphing one into another via a continuous path along which all points fall on the manifold; the ability to interpolate between samples is the key to understanding generalization in deep learning because it allows making sense of points never seen by relating them to nearby points on the manifold.