# Boosting Generative Models by Leveraging Cascaded Meta-Models

## TL;DR

This paper introduces a cascaded meta-model boosting framework for deep generative models, enabling improved modeling of complex data distributions through separate training and extension to semi-supervised learning.

## Contribution

It proposes a novel cascaded boosting approach for generative models that supports separate training of meta-models and extends to semi-supervised learning.

## Key findings

- Enhanced generative modeling of complex data.
- Effective separate training of meta-models.
- Improved performance when combined with multiplicative boosting.

## Abstract

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.