Competitive Training of Mixtures of Independent Deep Generative Models

Understand

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.