Denoising diffusion probabilistic models have recently demonstrated
state-of-the-art generative performance and been used as strong pixel-level
representation learners. This paper decomposes the interrelation between the
generative capability and representation learning ability inherent in diffusion
models. We present masked diffusion model (MDM), a scalable self-supervised
representation learner that substitutes the conventional additive Gaussian
noise of traditional diffusion with a masking mechanism. Our proposed approach
convincingly surpasses prior benchmarks, demonstrating remarkable advancements
in both medical and natural image semantic segmentation tasks, particularly
within the context of few-shot scenario.

最近，去噪扩散概率模型展示了最先进的生成性能，并被用作强大的像素级表示学习器。本文将扩散模型内在的生成能力与表示学习能力之间的相互关系进行了分解。我们提出了掩蔽扩散模型 (MDM)，这是一种可扩展的自监督表示学习器，用于将传统扩散中的加性高斯噪声替换为遮罩机制。我们所提出的方法在医学和自然图像语义分割任务中明显超越了先前的基准，并在少样本场景下展示了显著的进展。