Reviews: BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling

The authors propose variational autoencoder where inference is performed bidirectionally (top - bottom and bottom - top) with the intention of enhance the flow of information and avoid inactive units. This is achieved via multi-layered stochastic variables and a deterministic backbone network. The proposed inference model is akin to ladder VAE but with stochastic layers. The proposed model does not contain autorregressive elements. The authors present extensive results on image datasets and also consider semisupervised classification and outlier detection tasks.