A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
NeurInt : Learning to Interpolate through Neural ODEs
[article]
2021
arXiv
pre-print
A wide range of applications require learning image generation models whose latent space effectively captures the high-level factors of variation present in the data distribution. The extent to which a model represents such variations through its latent space can be judged by its ability to interpolate between images smoothly. However, most generative models mapping a fixed prior to the generated images lead to interpolation trajectories lacking smoothness and containing images of reduced
arXiv:2111.04123v1
fatcat:4zfiqe2odbfnnd4vybhtarzc64