Sampling Generative Networks

Understand

We introduce several techniques for sampling and visualizing the latent spaces of generative models.

• Replacing linear interpolation with spherical linear interpolation prevents diverging from a model's prior distribution and produces sharper samples.
• J-Diagrams and MINE grids are introduced as visualizations of manifolds created by analogies and nearest neighbors.
• We demonstrate two new techniques for deriving attribute vectors: bias-corrected vectors with data replication and synthetic vectors with data augmentation.