# A Simple Generative Network

arXiv: https://arxiv.org/abs/2106.09330 · HTML: https://mlsanity.com/paper/2106.09330/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [A note on the evaluation of generative models](https://mlsanity.com/paper/1511.01844/) (2015)
- [Generative Moment Matching Networks](https://mlsanity.com/paper/1502.02761/) (2015)
- [Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models](https://mlsanity.com/paper/2103.04922/) (2021)
- [Training generative neural networks via Maximum Mean Discrepancy optimization](https://mlsanity.com/paper/1505.03906/) (2015)
- [Learning in Implicit Generative Models](https://mlsanity.com/paper/1610.03483/) (2016)
- [Efficient Estimation of Mutual Information for Strongly Dependent Variables](https://mlsanity.com/paper/1411.2003/) (2014)
- [Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)](https://mlsanity.com/paper/1610.02455/) (2016)
- [On the Quantitative Analysis of Decoder-Based Generative Models](https://mlsanity.com/paper/1611.04273/) (2016)
- [Deep Generative Stochastic Networks Trainable by Backprop](https://mlsanity.com/paper/1306.1091/) (2013)

## Abstract (arXiv)

Generative neural networks are able to mimic intricate probability distributions such as those of handwritten text, natural images, etc. Since their inception several models were proposed. The most successful of these were based on adversarial (GAN), auto-encoding (VAE) and maximum mean discrepancy (MMD) relatively complex architectures and schemes. Surprisingly, a very simple architecture (a single feed-forward neural network) in conjunction with an obvious optimization goal (Kullback_Leibler divergence) was apparently overlooked. This paper demonstrates that such a model (denoted SGN for its simplicity) is able to generate samples visually and quantitatively competitive as compared with the fore-mentioned state of the art methods.