  Learning how to model complex scenes in a modular way with recombinable
components is a pre-requisite for higher-order reasoning and acting in the
physical world. However, current generative models lack the ability to capture
the inherently compositional and layered nature of visual scenes. While recent
work has made progress towards unsupervised learning of object-based scene
representations, most models still maintain a global representation space
(i.e., objects are not explicitly separated), and cannot generate scenes with
novel object arrangement and depth ordering. Here, we present an alternative
approach which uses an inductive bias encouraging modularity by training an
ensemble of generative models (experts). During training, experts compete for
explaining parts of a scene, and thus specialise on different object classes,
with objects being identified as parts that re-occur across multiple scenes.
Our model allows for controllable sampling of individual objects and
recombination of experts in physically plausible ways. In contrast to other
methods, depth layering and occlusion are handled correctly, moving this
approach closer to a causal generative scene model. Experiments on simple toy
data qualitatively demonstrate the conceptual advantages of the proposed
approach.
