Variational Learning of Disentangled Representations

Existing variational methods for learning disentangled representations often suffer from leakage between latent variables. We introduce DISCoVeR, a variational framework with a dual-latent architecture, parallel reconstruction paths, and a max-min objective that separates condition-invariant and condition-specific factors. Our method improves disentanglement on synthetic data, natural images, and single-cell RNA-seq data.