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：在大量数据上训练的大型单体生成模型已成为人工智能研究中日益占据主导地位的方法。本文主张，我们应转而通过组合较小的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使我们能够以更高效的数据利用方式学习分布，从而泛化到训练时未见过的数据分布部分。我们进一步展示了该方法如何使我们能够针对训练时完全未见过的任务，通过编程方式构建新的生成模型。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组件。