Masked Diffusion as Self-supervised Representation Learner
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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.
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