Task: Summarization
Input (English): One of the most successful techniques in generative models has been decomposing a complicated generation task into a series of simpler generation tasks.   For example, generating an image at a low resolution and then learning to refine that into a high resolution image often improves results substantially.   Here we explore a novel strategy for decomposing generation for complicated objects in which we first generate latent variables which describe a subset of the observed variables, and then map from these latent variables to the observed space.   We show that this allows us to achieve decoupled training of complicated generative models and present both theoretical and experimental results supporting the benefit of such an approach.  
Output (English): The paper investigates the potential of hierarchical latent variable models for generating images and image sequences and proposes to train several ALI models stacked on top of each other to create a hierarchical representation of the data.