# GENs: Generative Encoding Networks

arXiv: https://arxiv.org/abs/2010.15283 · HTML: https://mlsanity.com/paper/2010.15283/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [A Review of Change of Variable Formulas for Generative Modeling](https://mlsanity.com/paper/2308.02652/) (2023)
- [Matching aggregate posteriors in the variational autoencoder](https://mlsanity.com/paper/2311.07693/) (2023)
- [ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders](https://mlsanity.com/paper/2501.10901/) (2025)
- [Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions](https://mlsanity.com/paper/2501.15705/) (2025)
- [Unsupervised Domain Adaptation for Medical Image Segmentation via Feature-space Density Matching](https://mlsanity.com/paper/2305.05789/) (2023)
- [AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies](https://mlsanity.com/paper/2501.16760/) (2025)

## Abstract (arXiv)

Mapping data from and/or onto a known family of distributions has become an important topic in machine learning and data analysis. Deep generative models (e.g., generative adversarial networks ) have been used effectively to match known and unknown distributions. Nonetheless, when the form of the target distribution is known, analytical methods are advantageous in providing robust results with provable properties. In this paper, we propose and analyze the use of nonparametric density methods to estimate the Jensen-Shannon divergence for matching unknown data distributions to known target distributions, such Gaussian or mixtures of Gaussians, in latent spaces. This analytical method has several advantages: better behavior when training sample quantity is low, provable convergence properties, and relatively few parameters, which can be derived analytically. Using the proposed method, we enforce the latent representation of an autoencoder to match a target distribution in a learning framework that we call a em generative encoding network. Here, we present the numerical methods; derive the expected distribution of the data in the latent space; evaluate the properties of the latent space, sample reconstruction, and generated samples; show the advantages over the adversarial counterpart; and demonstrate the application of the method in real world.