Variational Learning of Disentangled Representations

Disentangled representations separate factors that are shared across conditions from those that are condition-specific.

Such separation is needed for generalization to new domains, treatments, patients, or species.

A dominant line of work pursues this goal through variational formulations.

While these approaches achieve partial disentanglement, they often exhibit three common limitations: they either do not remove all condition-specific information from the condition-specific representation, allow the condition-specific representation to become uninformative, or impose independence assumptions that do not reflect the underlying generative process.

In this work, we introduce DisCoVR, a variational framework that addresses these limitations.

Its objective is aligned with the probabilistic structure of the data-generating process, and includes an adversarial term that prevents condition-specific information from being encoded in the condition-specific this http URL reconstructs the data from both shared and condition-specific representations, ensuring that each remains informative, and uses a structured prior that further reinforces the informativeness of both representations.

We show that across synthetic, image, and single-cell RNA-sequencing datasets, DisCoVR achieves stronger disentanglement compared to previous approaches.