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.

本文研究二值神经网络在生成模型中的应用，通过开发一类新的二值权重规范化方法和提供针对这些二值化生成模型的架构设计，成功地训练出使用二值神经网络的生成模型，使得模型的计算成本大幅降低，但损失函数的值接近于原模型，模型体积更小且速度更快。

用二元神经网络降低深度生成模型的计算成本

Reducing the Computational Cost of Deep Generative Models with Binary  Neural Networks

Over the past decade deep learning has driven progress in 2D image
understanding. Despite these advancements, techniques for automatic 3D sensed
data understanding, such as point clouds, is comparatively immature. However,
with a range of important applications from indoor robotics navigation to
national scale remote sensing there is a high demand for algorithms that can
learn to automatically understand and classify 3D sensed data. In this paper we
review the current state-of-the-art deep learning architectures for processing
unstructured Euclidean data. We begin by addressing the background concepts and
traditional methodologies. We review the current main approaches including;
RGB-D, multi-view, volumetric and fully end-to-end architecture designs.
Datasets for each category are documented and explained. Finally, we give a
detailed discussion about the future of deep learning for 3D sensed data, using
literature to justify the areas where future research would be most valuable.

本文综述了当前处理非结构化欧几里得数据的深度学习架构，并提供了自动理解和分类三维感测数据的算法需求和未来研究价值的文献支持。