2 research outputs found
Fast Approximate Geodesics for Deep Generative Models
The length of the geodesic between two data points along a Riemannian
manifold, induced by a deep generative model, yields a principled measure of
similarity. Current approaches are limited to low-dimensional latent spaces,
due to the computational complexity of solving a non-convex optimisation
problem. We propose finding shortest paths in a finite graph of samples from
the aggregate approximate posterior, that can be solved exactly, at greatly
reduced runtime, and without a notable loss in quality. Our approach,
therefore, is hence applicable to high-dimensional problems, e.g., in the
visual domain. We validate our approach empirically on a series of experiments
using variational autoencoders applied to image data, including the Chair,
FashionMNIST, and human movement data sets.Comment: 28th International Conference on Artificial Neural Networks, 201