  A common assumption in causal modeling posits that the data is generated by a
set of independent mechanisms, and algorithms should aim to recover this
structure. Standard unsupervised learning, however, is often concerned with
training a single model to capture the overall distribution or aspects thereof.
Inspired by clustering approaches, we consider mixtures of implicit generative
models that ``disentangle'' the independent generative mechanisms underlying
the data. Relying on an additional set of discriminators, we propose a
competitive training procedure in which the models only need to capture the
portion of the data distribution from which they can produce realistic samples.
As a by-product, each model is simpler and faster to train. We empirically show
that our approach splits the training distribution in a sensible way and
increases the quality of the generated samples.
