VAE Explainer: Supplement Learning Variational Autoencoders with Interactive Visualization
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 13 references that are in the map. First posted on arXiv 13 Sep 2024.
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References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2019An Introduction to Variational Autoencoderscited by 997
- 2020CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualizationcited by 57
- 2020Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systemscited by 50
- 2023Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusioncited by 17
- 2024Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentationcited by 9
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