VAE Explainer: Supplement Learning Variational Autoencoders with Interactive Visualization
This tool addresses the challenge of making VAEs more accessible for learners and practitioners by providing an interactive supplement to existing static resources.
The paper tackles the problem of explaining Variational Autoencoders (VAEs) by introducing VAE Explainer, an interactive browser-based visualization tool that supplements static documentation with interactive model inputs, latent space, and output, connecting high-level understanding with implementation through annotated code and a live computational graph.
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.