Don't be so negative! Score-based Generative Modeling with Oracle-assisted Guidance
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866912477434871808 |
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| author | Naderiparizi, Saeid Liang, Xiaoxuan Cohan, Setareh Zwartsenberg, Berend Wood, Frank |
| author_facet | Naderiparizi, Saeid Liang, Xiaoxuan Cohan, Setareh Zwartsenberg, Berend Wood, Frank |
| contents | Score-based diffusion models are a powerful class of generative models, widely utilized across diverse domains. Despite significant advancements in large-scale tasks such as text-to-image generation, their application to constrained domains has received considerably less attention. This work addresses model learning in a setting where, in addition to the training dataset, there further exists side-information in the form of an oracle that can label samples as being outside the support of the true data generating distribution. Specifically we develop a new denoising diffusion probabilistic modeling methodology, Gen-neG, that leverages this additional side-information. Gen-neG builds on classifier guidance in diffusion models to guide the generation process towards the positive support region indicated by the oracle. We empirically establish the utility of Gen-neG in applications including collision avoidance in self-driving simulators and safety-guarded human motion generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_16463 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Don't be so negative! Score-based Generative Modeling with Oracle-assisted Guidance Naderiparizi, Saeid Liang, Xiaoxuan Cohan, Setareh Zwartsenberg, Berend Wood, Frank Machine Learning Score-based diffusion models are a powerful class of generative models, widely utilized across diverse domains. Despite significant advancements in large-scale tasks such as text-to-image generation, their application to constrained domains has received considerably less attention. This work addresses model learning in a setting where, in addition to the training dataset, there further exists side-information in the form of an oracle that can label samples as being outside the support of the true data generating distribution. Specifically we develop a new denoising diffusion probabilistic modeling methodology, Gen-neG, that leverages this additional side-information. Gen-neG builds on classifier guidance in diffusion models to guide the generation process towards the positive support region indicated by the oracle. We empirically establish the utility of Gen-neG in applications including collision avoidance in self-driving simulators and safety-guarded human motion generation. |
| title | Don't be so negative! Score-based Generative Modeling with Oracle-assisted Guidance |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2307.16463 |