Hierarchically branched diffusion models leverage dataset structure for class-conditional generation
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| _version_ | 1866910315060396032 |
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| author | Tseng, Alex M. Shen, Max Biancalani, Tommaso Scalia, Gabriele |
| author_facet | Tseng, Alex M. Shen, Max Biancalani, Tommaso Scalia, Gabriele |
| contents | Class-labeled datasets, particularly those common in scientific domains, are rife with internal structure, yet current class-conditional diffusion models ignore these relationships and implicitly diffuse on all classes in a flat fashion. To leverage this structure, we propose hierarchically branched diffusion models as a novel framework for class-conditional generation. Branched diffusion models rely on the same diffusion process as traditional models, but learn reverse diffusion separately for each branch of a hierarchy. We highlight several advantages of branched diffusion models over the current state-of-the-art methods for class-conditional diffusion, including extension to novel classes in a continual-learning setting, a more sophisticated form of analogy-based conditional generation (i.e. transmutation), and a novel interpretability into the generation process. We extensively evaluate branched diffusion models on several benchmark and large real-world scientific datasets spanning many data modalities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_10777 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Hierarchically branched diffusion models leverage dataset structure for class-conditional generation Tseng, Alex M. Shen, Max Biancalani, Tommaso Scalia, Gabriele Machine Learning Artificial Intelligence Class-labeled datasets, particularly those common in scientific domains, are rife with internal structure, yet current class-conditional diffusion models ignore these relationships and implicitly diffuse on all classes in a flat fashion. To leverage this structure, we propose hierarchically branched diffusion models as a novel framework for class-conditional generation. Branched diffusion models rely on the same diffusion process as traditional models, but learn reverse diffusion separately for each branch of a hierarchy. We highlight several advantages of branched diffusion models over the current state-of-the-art methods for class-conditional diffusion, including extension to novel classes in a continual-learning setting, a more sophisticated form of analogy-based conditional generation (i.e. transmutation), and a novel interpretability into the generation process. We extensively evaluate branched diffusion models on several benchmark and large real-world scientific datasets spanning many data modalities. |
| title | Hierarchically branched diffusion models leverage dataset structure for class-conditional generation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2212.10777 |