Boosted Generative Models

Understand

We propose a novel approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes.

• Our meta-algorithmic framework can leverage any existing base learner that permits likelihood evaluation, including recent deep expressive models.
• Further, our approach allows the ensemble to include discriminative models trained to distinguish real data from model-generated data.
• We show theoretical conditions under which incorporating a new model in the ensemble will improve the fit and empirically demonstrate the effectiveness of our black-box boosting algorithms on density estimation, classification, and sample generation on benchmark datasets for a wide range of generative models.