Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors

Understand

While deep representation learning has become increasingly capable of separating task-relevant representations from other confounding factors in the data, two significant challenges remain.

• First, there is often an unknown and potentially infinite number of confounding factors coinciding in the data.
• Second, not all of these factors are readily observable.
• In this paper, we present a deep conditional generative model that learns to disentangle a task-relevant representation from an unknown number of confounding factors that may grow infinitely.