Ferenc Huszár • 3/18/2021

An information maximization view on the $\beta$-VAE objective

This article provides a deep, technical analysis of the β-VAE (Variational Autoencoder) objective, deriving it from first principles of information maximization. It explains how the β hyperparameter controls the trade-off between reconstruction accuracy and the KL divergence penalty, and how this encourages the learning of disentangled latent representations by promoting coordinate-wise conditional independence in the posterior distribution.

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#Deep Learning #Generative Models #Vae

$An information maximization view on the $\beta$-VAE objective$