Masked Diffusion as Self-supervised Representation Learner
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916203997429760 |
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| author | Pan, Zixuan Chen, Jianxu Shi, Yiyu |
| author_facet | Pan, Zixuan Chen, Jianxu Shi, Yiyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_05695 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Masked Diffusion as Self-supervised Representation Learner Pan, Zixuan Chen, Jianxu Shi, Yiyu Computer Vision and Pattern Recognition 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. |
| title | Masked Diffusion as Self-supervised Representation Learner |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2308.05695 |