  In this work we present Discrete Attend Infer Repeat (Discrete-AIR), a
Recurrent Auto-Encoder with structured latent distributions containing discrete
categorical distributions, continuous attribute distributions, and factorised
spatial attention. While inspired by the original AIR model andretaining AIR
model's capability in identifying objects in an image, Discrete-AIR provides
direct interpretability of the latent codes. We show that for Multi-MNIST and a
multiple-objects version of dSprites dataset, the Discrete-AIR model needs just
one categorical latent variable, one attribute variable (for Multi-MNIST only),
together with spatial attention variables, for efficient inference. We perform
analysis to show that the learnt categorical distributions effectively capture
the categories of objects in the scene for Multi-MNIST and for Multi-Sprites.
