  Interpolations in the latent space of deep generative models is one of the
standard tools to synthesize semantically meaningful mixtures of generated
samples. As the generator function is non-linear, commonly used linear
interpolations in the latent space do not yield the shortest paths in the
sample space, resulting in non-smooth interpolations. Recent work has therefore
equipped the latent space with a suitable metric to enforce shortest paths on
the manifold of generated samples. These are often, however, susceptible of
veering away from the manifold of real samples, resulting in smooth but
unrealistic generation that requires an additional method to assess the sample
quality along paths. Generative Adversarial Networks (GANs), by construction,
measure the sample quality using its discriminator network. In this paper, we
establish that the discriminator can be used effectively to avoid regions of
low sample quality along shortest paths. By reusing the discriminator network
to modify the metric on the latent space, we propose a lightweight solution for
improved interpolations in pre-trained GANs.
