  Deep generative models are effective methods of modeling data. However, it is
not easy for a single generative model to faithfully capture the distributions
of complex data such as images. In this paper, we propose an approach for
boosting generative models, which cascades meta-models together to produce a
stronger model. Any hidden variable meta-model (e.g., RBM and VAE) which
supports likelihood evaluation can be leveraged. We derive a decomposable
variational lower bound of the boosted model, which allows each meta-model to
be trained separately and greedily. Besides, our framework can be extended to
semi-supervised boosting, where the boosted model learns a joint distribution
of data and labels. Finally, we combine our boosting framework with the
multiplicative boosting framework, which further improves the learning power of
generative models.
