Papers › Masked Diffusion as Self-supervised Representation Learner
Masked Diffusion as Self-supervised Representation Learner
Zixuan Pan, Jianxu Chen, Yiyu Shi
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Medical Image Segmentation | GlaS | MDM | Dice | 91.95 | #2 of 10 | Archive leaderboard | report |
| Medical Image Segmentation | GlaS | MDM | F1 | 91.95 | #2 of 10 | Archive leaderboard | report |
| Medical Image Segmentation | GlaS | MDM | IoU | 85.13 | #2 of 10 | Archive leaderboard | report |
| Medical Image Segmentation | MoNuSeg | MDM | F1 | 81.01 | #5 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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