  Deep generative models provide a powerful set of tools to understand
real-world data. But as these models improve, they increase in size and
complexity, so their computational cost in memory and execution time grows.
Using binary weights in neural networks is one method which has shown promise
in reducing this cost. However, whether binary neural networks can be used in
generative models is an open problem. In this work we show, for the first time,
that we can successfully train generative models which utilize binary neural
networks. This reduces the computational cost of the models massively. We
develop a new class of binary weight normalization, and provide insights for
architecture designs of these binarized generative models. We demonstrate that
two state-of-the-art deep generative models, the ResNet VAE and Flow++ models,
can be binarized effectively using these techniques. We train binary models
that achieve loss values close to those of the regular models but are 90%-94%
smaller in size, and also allow significant speed-ups in execution time.
