Representing Closed Transformation Paths in Encoded Network Latent Space

Understand

Deep generative networks have been widely used for learning mappings from a low-dimensional latent space to a high-dimensional data space.

• In many cases, data transformations are defined by linear paths in this latent space.
• However, the Euclidean structure of the latent space may be a poor match for the underlying latent structure in the data.
• In this work, we incorporate a generative manifold model into the latent space of an autoencoder in order to learn the low-dimensional manifold structure from the data and adapt the latent space to accommodate this structure.