With the recent rapid progress in the study of deep generative models (DGMs), there is a need for a framework that can implement them in a simple and generic way. In this research, we focus on two features of the latest DGMs: (1) deep neural networks are encapsulated by probability distributions and (2) models are designed and learned based on an objective function. Taking these features into account, we propose a new DGM library called Pixyz. We experimentally show that our library is faster than existing probabilistic modeling languages in learning simple DGMs and we show that our library can be used to implement complex DGMs in a simple and concise manner, which is difficult to do with existing libraries.

翻译：随着最近深层基因模型研究(DGM)的快速进展,需要有一个能够以简单和通用方式实施这些模型的框架。在这个研究中,我们把重点放在最新的DGM的两个特征上:(1) 深神经网络被概率分布所包涵,(2) 模型是根据客观功能设计和学习的。考虑到这些特征,我们建议建立一个名为Pixyz的新的DGM图书馆。我们实验性地显示,在学习简单的DGM中,我们的图书馆比现有的概率模拟语言更快,我们显示,我们的图书馆可以用简单简洁的方式实施复杂的DGM,这与现有的图书馆是很难做到的。