Learning Manifold Dimensions with Conditional Variational Autoencoders

Moreover, it remains unclear how such considerations would change when various types of conditioning variables are introduced, or when the data support is extended to a union of manifolds (e.g., as is likely the case for MNIST digits and related). In this work, we address these points by first proving that V AE global minima are indeed capable of recovering the correct manifold dimension.