Advances in unsupervised learning enable reconstruction and generation of samples from complex distributions, but this success is marred by the inscrutability of the representations learned. We propose an information-theoretic approach to characterizing disentanglement and dependence in representation learning using multivariate mutual information, also called total correlation. The principle of total Cor-relation Ex-planation (CorEx) has motivated successful unsupervised learning applications across a variety of domains, but under some restrictive assumptions. Here we relax those restrictions by introducing a flexible variational lower bound to CorEx. Surprisingly, we find that this lower bound is equivalent to the one in variational autoencoders (VAE) under certain conditions. This information-theoretic view of VAE deepens our understanding of hierarchical VAE and motivates a new algorithm, AnchorVAE, that makes latent codes more interpretable through information maximization and enables generation of richer and more realistic samples.

本文提出了一种基于多元互信息或称总相关性的信息论方法，用于表征表示学习中的解缠和依赖性，提出了一种基于可变下限的CorEx方法，使得Latent codes更具可解释性，并且能够生成更丰富、更真实的样本；同时，将VAE与CorEx相联系，在信息论上加深了对分层VAE的理解，并激发了一种新的算法AnchorVAE。

自动编码总相关性解释