Compositional Generative Modeling: A Single Model is Not All You Need
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要