We introduce a joint diffusion model that simultaneously learns meaningful internal representations fit for both generative and predictive tasks. Joint machine learning models that allow synthesizing and classifying data often offer uneven performance between those tasks or are unstable to train. In this work, we depart from a set of empirical observations that indicate the usefulness of internal representations built by contemporary deep diffusion-based generative models in both generative and predictive settings. We then introduce an extension of the vanilla diffusion model with a classifier that allows for stable joint training with shared parametrization between those objectives. The resulting joint diffusion model offers superior performance across various tasks, including generative modeling, semi-supervised classification, and domain adaptation.

翻译：我们提出了一种联合扩散模型，该模型能够同时学习适用于生成任务和预测任务的有意义的内部表征。现有的联合机器学习模型在实现数据合成与分类时，往往在生成任务和预测任务之间表现不均衡，或者训练过程不稳定。在本文中，我们从一组实证观察出发，这些观察表明当代基于深度扩散的生成模型所构建的内部表征在生成和预测场景中均具有实用性。随后，我们引入了一种带有分类器的标准扩散模型扩展，该扩展能够实现面向共享参数目标的稳定联合训练。由此产生的联合扩散模型在包括生成建模、半监督分类和域适应在内的多种任务中均展现出优越的性能。