VAE Explainer: Supplement Learning Variational Autoencoders with Interactive Visualization

TL;DR

VAE Explainer is an interactive browser-based tool that enhances understanding of Variational Autoencoders by providing visualizations, interactive model components, and linked code, making VAE concepts more accessible and easier to grasp.

Contribution

It introduces an interactive visualization tool for VAEs that complements static explanations with live, manipulable visualizations and code integration.

Findings

Improves understanding of VAEs through interactivity.

Provides a live, browser-based visualization tool.

Open source implementation available for community use.

Abstract

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.