  Denoising diffusion probabilistic models have recently demonstrated
state-of-the-art generative performance and have 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 the masked diffusion model (MDM), a scalable
self-supervised representation learner for semantic segmentation, substituting
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 in few-shot scenarios.
