Diffusion Probabilistic Models (DPMs) have recently demonstrated impressive
results on various generative tasks.Despite its promises, the learned
representations of pre-trained DPMs, however, have not been fully understood.
In this paper, we conduct an in-depth investigation of the representation power
of DPMs, and propose a novel knowledge transfer method that leverages the
knowledge acquired by generative DPMs for recognition tasks. Our study begins
by examining the feature space of DPMs, revealing that DPMs are inherently
denoising autoencoders that balance the representation learning with
regularizing model capacity. To this end, we introduce a novel knowledge
transfer paradigm named RepFusion. Our paradigm extracts representations at
different time steps from off-the-shelf DPMs and dynamically employs them as
supervision for student networks, in which the optimal time is determined
through reinforcement learning. We evaluate our approach on several image
classification, semantic segmentation, and landmark detection benchmarks, and
demonstrate that it outperforms state-of-the-art methods. Our results uncover
the potential of DPMs as a powerful tool for representation learning and
provide insights into the usefulness of generative models beyond sample
generation. The code is available at
https://github.com/Adamdad/Repfusion.

通过深入研究 DPM 的表示能力，提出了一种名为 Repfusion 的新型知识迁移方法，该方法利用生成型 DPM 获取的知识用于识别任务，通过强化学习动态地根据不同的时间步骤从现成 DPM 中提取表示并作为学生网络的监督，实验证明此方法在多个任务中优于现有方法，揭示了 DPM 作为表示学习工具的潜力和生成模型在样本生成以外的用途。