VAE Explainer: Supplement Learning Variational Autoencoders with Interactive Visualization

2024/09/13 by Donald Bertucci, Bertucci, Donald, Alex Endert +1 · 1 citation
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Computational and Text Analysis Methods #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2409.09011

openalex publication_date 2024/09/13 · openalex created_date 2024/10/23 · openalex updated_date 2026/07/28

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.

Cited by

• Designing with Deception: ML- and Covert Gate-Enhanced Camouflaging to Thwart IC Reverse Engineering

Related

• Tutorial on Variational Autoencoders
• Inducing Interpretable Representations with Variational Autoencoders
• VAE-CE: Visual Contrastive Explanation using Disentangled VAEs
• Using VAEs to Learn Latent Variables: Observations on Applications in cryo-EM
• Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders