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We introduce a new approach to learning in hierarchical latent-variablegenerative models called the "distributed distributional code Helmholtzmachine", which emphasises flexibility and accuracy in the inferential process.In common with the original Helmholtz machine and later variational autoencoderalgorithms (but unlike adverserial methods) our approach learns an explicitinference or "recognition" model to approximate the posterior distribution overthe latent variables. Unlike in these earlier methods, the posteriorrepresentation is not limited to a narrow tractable parameterised form (nor isit represented by samples). To train the generative and recognition models wedevelop an extended wake-sleep algorithm inspired by the original HelmholtzMachine. This makes it possible to learn hierarchical latent models with bothdiscrete and continuous variables, where an accurate posterior representationis essential. We demonstrate that the new algorithm outperforms currentstate-of-the-art methods on synthetic, natural image patch and the MNIST datasets.

Flexible and accurate inference and learning for deep generative models