# VAE Explainer: Supplement Learning Variational Autoencoders with Interactive Visualization

arXiv: https://arxiv.org/abs/2409.09011 · HTML: https://mlsanity.com/paper/2409.09011/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization](https://mlsanity.com/paper/2004.15004/) (2020)
- [Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems](https://mlsanity.com/paper/2012.03448/) (2020)
- [Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion](https://mlsanity.com/paper/2305.03509/) (2023)
- [Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation](https://mlsanity.com/paper/2408.04619/) (2024)

## Abstract (arXiv)

Variational Autoencoders are widespread in Machine Learning, but are typically explained with dense math notation or static code examples. This paper presents VAE Explainer, an interactive Variational Autoencoder running in the browser to supplement existing static documentation (e.g., Keras Code Examples). VAE Explainer adds interactions to the VAE summary with interactive model inputs, latent space, and output. VAE Explainer connects the high-level understanding with the implementation: annotated code and a live computational graph. The VAE Explainer interactive visualization is live at https://xnought.github.io/vae-explainer and the code is open source at https://github.com/xnought/vae-explainer.