VAE Explainer: Supplement Learning Variational Autoencoders with Interactive Visualization

Donald Bertucci, Alex Endert
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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.
VAE Explainer:利用交互式可视化补充学习变异自动编码器
变分自动编码器在机器学习领域非常普遍,但通常使用密集的数学符号或静态代码示例进行解释。本文介绍了 VAE Explainer,这是一种在浏览器中运行的交互式变分自动编码器,用于补充现有的静态文档(如 Keras 代码示例)。VAE Explainer 通过交互式模型输入、潜在空间和输出为 VAE 摘要添加了交互。VAE Explainer 将高层次的理解与实现联系起来:注释代码和实时计算图。VAE Explainer 交互式可视化可在https://xnought.github.io/vae-explainer 上运行,代码可在https://github.com/xnought/vae-explainer 上开源。
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