用浏览器交互可视化解释VAE，让抽象模型变直观。
VAE Explainer: Supplement Learning Variational Autoencoders with Interactive Visualization
- 通过交互式输入、隐空间和输出展示VAE工作原理
- 集成注释代码与实时计算图，连接理论与实现
- 适合想理解VAE机制的初学者和教学使用
变分自编码器在机器学习中广泛应用，但通常仅以密集的数学符号或静态代码示例解释。本文提出 VAE Explainer，一个可在浏览器中运行的交互式变分自编码器，用于补充现有静态文档（如 Keras 代码示例）。该工具通过交互式输入、隐空间和输出展示，将高层理解与实现连接起来：提供注释代码和实时计算图。VAE Explainer 的交互可视化已上线（https://xnought.github.io/vae-explainer），代码开源（https://github.com/xnought/vae-explainer）。
原文摘要 · Abstract (English)
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.
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