# Sampling Generative Networks

arXiv: https://arxiv.org/abs/1609.04468 · HTML: https://mlsanity.com/paper/1609.04468/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2016.

## Built on by (most cited first, 5 of 5)

- [Provable Compressed Sensing with Generative Priors via Langevin Dynamics](https://mlsanity.com/paper/2102.12643/) (2021)
- [A Behavior Regularized Implicit Policy for Offline Reinforcement Learning](https://mlsanity.com/paper/2202.09673/) (2022)
- [JRadiEvo: A Japanese Radiology Report Generation Model Enhanced by Evolutionary Optimization of Model Merging](https://mlsanity.com/paper/2411.09933/) (2024)
- [PetroGAN: A novel GAN-based approach to generate realistic, label-free petrographic datasets](https://mlsanity.com/paper/2204.05114/) (2022)
- [NeurInt : Learning to Interpolate through Neural ODEs](https://mlsanity.com/paper/2111.04123/) (2021)

## Abstract (arXiv)

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. Binary classification using attribute vectors is presented as a technique supporting quantitative analysis of the latent space. Most techniques are intended to be independent of model type and examples are shown on both Variational Autoencoders and Generative Adversarial Networks.