%0 Generic %1 gao2018autoencoding %A Gao, Shuyang %A Brekelmans, Rob %A Steeg, Greg Ver %A Galstyan, Aram %D 2018 %K corex fulltext machine-learning shouldread statistics %T Auto-Encoding Total Correlation Explanation %U http://arxiv.org/abs/1802.05822 %X 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.

@misc{gao2018autoencoding, abstract = {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.}, added-at = {2018-10-04T13:52:14.000+0200}, author = {Gao, Shuyang and Brekelmans, Rob and Steeg, Greg Ver and Galstyan, Aram}, biburl = {https://www.bibsonomy.org/bibtex/22c5b4918fbdeb9866e27d6961612c4b7/marcsaric}, description = {[1802.05822] Auto-Encoding Total Correlation Explanation}, interhash = {1b82326b8ce1b78d793d2ddc10f8eea9}, intrahash = {2c5b4918fbdeb9866e27d6961612c4b7}, keywords = {corex fulltext machine-learning shouldread statistics}, note = {cite arxiv:1802.05822}, timestamp = {2018-10-04T13:52:14.000+0200}, title = {Auto-Encoding Total Correlation Explanation}, url = {http://arxiv.org/abs/1802.05822}, year = 2018 }