Authors: Donald Bertucci, Alex Endert
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.
Source: http://arxiv.org/abs/2409.09011v1
