Large monolithic generative models trained on massive amounts of data have become an increasingly dominant approach in AI research. In this paper, we argue that we should instead construct large generative systems by composing smaller generative models together. We show how such a compositional generative approach enables us to learn distributions in a more data-efficient manner, enabling generalization to parts of the data distribution unseen at training time. We further show how this enables us to program and construct new generative models for tasks completely unseen at training. Finally, we show that in many cases, we can discover separate compositional components from data.

翻译：大规模单体生成模型在大量数据上的训练已成为人工智能研究中越来越主流的方法。本文中，我们主张应通过组合较小生成模型来构建大型生成系统。我们展示了这种组合式生成方法如何能够以更高效的数据利用方式学习数据分布，从而实现对训练时未见数据分布部分的泛化。进一步地，我们说明该方法如何使我们能够为训练时完全未见的新任务编程并构建新型生成模型。最后，我们证明在许多情况下，可以从数据中自动发现独立的组合式组件。