Learning Finite-Dimensional Coding Schemes with Nonlinear Reconstruction Maps
Learning Finite-Dimensional Coding Schemes with Nonlinear Reconstruction Maps
This paper generalizes the Maurer--Pontil framework of finite-dimensional lossy coding schemes to the setting where a high-dimensional random vector is mapped to an element of a compact set of latent representations in a lower-dimensional Euclidean space, and the reconstruction map belongs to a given class of nonlinear maps. Under this …